scholarly journals DrugCombDB: a comprehensive database of drug combinations toward network medicine and combination therapy

2018 ◽  
Author(s):  
Lei Deng ◽  
Bo Zou ◽  
Wenhao Zhang ◽  
Hui Liu
Keyword(s):  
High Throughput ◽  
Drug Combination ◽  
High Throughput Screening ◽  
Prediction Models ◽  
Human Cancer ◽  
Drug Combinations ◽  
Data Sources ◽  
Supporting Evidence ◽  
Comprehensive Database ◽  
Computational Screening

AbstractDrug combinations have demonstrated high efficacy and low adverse side effects compared to single drug administrations in cancer therapies, and thus draw intensive attentions from researchers and pharmaceutical enterprises. Thanks to the fast development of high-throughput screening (HTS) methods, the amount of available drug combination datasets has tremendously increased. However, existing drug combination databases are lack of indications of the drug combinations and quantitative dose-responses. Therefore, there is an urgent need for a comprehensive database that is crucial to both experimental and computational screening of drug combinations. In this paper, we present DrugCombDB, a comprehensive database dedicated to integrating drug combinations from various data sources. Concretely, the data sources include 1) high-throughput screening assays of drug combinations, 2) external databases, and 3) manual curations from PubMed literature. In total, DrugCombDB includes 1,127,969 experimental data points with quantitative dose response and concentrations of drug combinations covering 561 unique drugs and 104 human cancer cell lines, and 1,875 FDA approved or literature-supported drug combinations. In particular, we adopted the zero interaction potency (ZIP) model [2] to compute the scores determining the synergy or antagonism of two drugs. To facilitate the downstream usage of our data resource, we prepared multiple datasets that are ready for building prediction models of classification and regression analysis. A website with user-friendly data visualization is provided to help users access the wealth of data. Users can input a drug of interest to retrieve associated drug combinations, together with the supporting evidence sources and drug targets. Our database is available at http://drugcombdb.denglab.org/.

scholarly journals DrugCombDB: a comprehensive database of drug combinations toward the discovery of combinatorial therapy

2019 ◽  
Author(s):  
Hui Liu ◽  
Wenhao Zhang ◽  
Bo Zou ◽  
Jinxian Wang ◽  
Yuanyuan Deng ◽  
...  
Keyword(s):  
High Throughput Screening ◽  
Computational Models ◽  
Prediction Models ◽  
Human Cancer ◽  
Rapid Development ◽  
Drug Combinations ◽  
Human Cancer Cell Lines ◽  
Related Data ◽  
Comprehensive Database ◽  
Computational Screening

Abstract Drug combinations have demonstrated high efficacy and low adverse side effects compared to single drug administration in cancer therapies and thus have drawn intensive attention from researchers and pharmaceutical enterprises. Due to the rapid development of high-throughput screening (HTS), the number of drug combination datasets available has increased tremendously in recent years. Therefore, there is an urgent need for a comprehensive database that is crucial to both experimental and computational screening of synergistic drug combinations. In this paper, we present DrugCombDB, a comprehensive database devoted to the curation of drug combinations from various data sources: (i) HTS assays of drug combinations; (ii) manual curations from the literature; and (iii) FDA Orange Book and external databases. Specifically, DrugCombDB includes 448 555 drug combinations derived from HTS assays, covering 2887 unique drugs and 124 human cancer cell lines. In particular, DrugCombDB has more than 6000 000 quantitative dose responses from which we computed multiple synergy scores to determine the overall synergistic or antagonistic effects of drug combinations. In addition to the combinations extracted from existing databases, we manually curated 457 drug combinations from thousands of PubMed publications. To benefit the further experimental validation and development of computational models, multiple datasets that are ready to train prediction models for classification and regression analysis were constructed and other significant related data were gathered. A website with a user-friendly graphical visualization has been developed for users to access the wealth of data and download prebuilt datasets. Our database is available at http://drugcombdb.denglab.org/.

scholarly journals Methods for High-Throughput Drug Combination Screening and Synergy Scoring

2016 ◽  
Author(s):  
Liye He ◽  
Evgeny Kulesskiy ◽  
Jani Saarela ◽  
Laura Turunen ◽  
Krister Wennerberg ◽  
...  
Keyword(s):  
Cancer Cells ◽  
High Throughput ◽  
Drug Combination ◽  
High Throughput Screening ◽  
Treatment Options ◽  
Drug Combinations ◽  
Molecular Medicine ◽  
Targeted Drugs ◽  
Functional Profiling ◽  
Automated Screening

AbstractGene products or pathways that are aberrantly activated in cancer but not in normal tissue hold great promises for being effective and safe anticancer therapeutic targets. Many targeted drugs have entered clinical trials but so far showed limited efficacy mostly due to variability in treatment responses and often rapidly emerging resistance. Towards more effective treatment options, we will critically need multi-targeted drugs or drug combinations, which selectively inhibit the cancer cells and block distinct escape mechanisms for the cells to become resistant. Functional profiling of drug combinations requires careful experimental design and robust data analysis approaches. At the Institute for Molecular Medicine Finland (FIMM), we have developed an experimental-computational pipeline for high-throughput screening of drug combination effects in cancer cells. The integration of automated screening techniques with advanced synergy scoring tools allows for efficient and reliable detection of synergistic drug interactions within a specific window of concentrations, hence accelerating the identification of potential drug combinations for further confirmatory studies.

scholarly journals High-Throughput 3D Tumor Spheroid Array Platform for Evaluating Sensitivity of Proton-Drug Combinations

2022 ◽  
Vol 23 (2) ◽  
pp. 587
Author(s):  
Dong Woo Lee ◽  
Jung Eun Kim ◽  
Ga-Haeng Lee ◽  
Arang Son ◽  
Hee Chul Park ◽  
...  
Keyword(s):  
Cell Lines ◽  
High Throughput ◽  
High Throughput Screening ◽  
Quantitative Data ◽  
Survival Rates ◽  
Drug Combinations ◽  
Targeted Drugs ◽  
Tumor Spheroid ◽  
Array Platform ◽  
Hnscc Cell

Proton beam therapy (PBT) is a critical treatment modality for head and neck squamous cell carcinoma (HNSCC). However, not much is known about drug combinations that may improve the efficacy of PBT. This study aimed to test the feasibility of a three-dimensional (3D) tumor-spheroid-based high-throughput screening platform that could assess cellular sensitivity against PBT. Spheroids of two HNSCC cell lines—Fadu and Cal27—cultured with a mixture of Matrigel were arrayed on a 384-pillar/well plate, followed by exposure to graded doses of protons or targeted drugs including olaparib at various concentrations. Calcein staining of HNSCC spheroids revealed a dose-dependent decrease in cell viability for proton irradiation or multiple targeted drugs, and provided quantitative data that discriminated the sensitivity between the two HNSCC cell lines. The combined effect of protons and olaparib was assessed by calculating the combination index from the survival rates of 4 × 4 matrices, showing that Cal27 spheroids had greater synergy with olaparib than Fadu spheroids. In contrast, adavosertib did not synergize with protons in both spheroids. Taken together, we demonstrated that the 3D pillar/well array platform was a useful tool that provided rapid, quantitative data for evaluating sensitivity to PBT and drug combinations. Our results further supported that administration of the combination of PBT and olaparib may be an effective treatment strategy for HNSCC patients.

scholarly journals Computational High-Throughput Screening of Polymeric Photocatalysts: Exploring the Effect of Composition, Sequence Isomerism and Conformational Degrees of Freedom

2018 ◽  
Author(s):  
isabelle Heath-Apostolopoulos ◽  
Liam Wilbraham ◽  
Martijn Zwijnenburg
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Degrees Of Freedom ◽  
Chemical Space ◽  
Low Cost ◽  
Computational Screening ◽  
Computational Workflow ◽  
Conformational Degrees Of Freedom

We discuss a low-cost computational workflow for the high-throughput screening of polymeric photocatalysts and demonstrate its utility by applying it to a number of challenging problems that would be difficult to tackle otherwise. Specifically we show how having access to a low-cost method allows one to screen a vast chemical space, as well as to probe the effects of conformational degrees of freedom and sequence isomerism. Finally, we discuss both the opportunities of computational screening in the search for polymer photocatalysts, as well as the biggest challenges.

scholarly journals Unbiased High-Throughput Drug Combination Pilot Screening Identifies Synergistic Drug Combinations Effective against Patient-Derived and Drug-Resistant Melanoma Cell Lines

2020 ◽  
pp. 247255522097091
Author(s):  
David A. Close ◽  
John M. Kirkwood ◽  
Ronald J. Fecek ◽  
Walter J. Storkus ◽  
Paul A. Johnston
Keyword(s):  
Growth Inhibition ◽  
Cell Lines ◽  
Melanoma Cell ◽  
High Throughput ◽  
Drug Combination ◽  
High Throughput Screening ◽  
Dna Methyltransferase ◽  
Cross Resistance ◽  
Braf Mutations ◽  
Melanoma Cell Lines

We describe the development, optimization, and validation of 384-well growth inhibition assays for six patient-derived melanoma cell lines (PDMCLs), three wild type (WT) for BRAF and three with V600E- BRAF mutations. We conducted a pilot drug combination (DC) high-throughput screening (HTS) of 45 pairwise 4×4 DC matrices prepared from 10 drugs in the PDMCL assays: two B-Raf inhibitors (BRAFi), a MEK inhibitor (MEKi), and a methylation agent approved for melanoma; cytotoxic topoisomerase II and DNA methyltransferase chemotherapies; and drugs targeting the base excision DNA repair enzyme APE1 (apurinic/apyrimidinic endonuclease-1/redox effector factor-1), SRC family tyrosine kinases, the heat shock protein 90 (HSP90) molecular chaperone, and histone deacetylases. Pairwise DCs between dasatinib and three drugs approved for melanoma therapy—dabrafenib, vemurafenib, or trametinib—were flagged as synergistic in PDMCLs. Exposure to fixed DC ratios of the SRC inhibitor dasatinib with the BRAFis or MEKis interacted synergistically to increase PDMCL sensitivity to growth inhibition and enhance cytotoxicity independently of PDMCL BRAF status. These DCs synergistically inhibited the growth of mouse melanoma cell lines that either were dabrafenib-sensitive or had acquired resistance to dabrafenib with cross resistance to vemurafenib, trametinib, and dasatinib. Dasatinib DCs with dabrafenib, vemurafenib, or trametinib activated apoptosis and increased cell death in melanoma cells independently of their BRAF status or their drug resistance phenotypes. These preclinical in vitro studies provide a data-driven rationale for the further investigation of DCs between dasatinib and BRAFis or MEKis as candidates for melanoma combination therapies with the potential to improve outcomes and/or prevent or delay the emergence of disease resistance.

SoftBV – a software tool for screening the materials genome of inorganic fast ion conductors

2019 ◽  
Vol 75 (1) ◽  
pp. 18-33 ◽  
Author(s):  
Haomin Chen ◽  
Lee Loong Wong ◽  
Stefan Adams
Keyword(s):  
Force Field ◽  
High Throughput ◽  
High Throughput Screening ◽  
Density Functional ◽  
Inorganic Compounds ◽  
Computational Cost ◽  
Lewis Base ◽  
Computational Screening ◽  
Wide Range ◽  
Dft Simulations

The identification of materials for advanced energy-storage systems is still mostly based on experimental trial and error. Increasingly, computational tools are sought to accelerate materials discovery by computational predictions. Here are introduced a set of computationally inexpensive software tools that exploit the bond-valence-based empirical force field previously developed by the authors to enable high-throughput computational screening of experimental or simulated crystal-structure models of battery materials predicting a variety of properties of technological relevance, including a structure plausibility check, surface energies, an inventory of equilibrium and interstitial sites, the topology of ion-migration paths in between those sites, the respective migration barriers and the site-specific attempt frequencies. All of these can be predicted from CIF files of structure models at a minute fraction of the computational cost of density functional theory (DFT) simulations, and with the added advantage that all the relevant pathway segments are analysed instead of arbitrarily predetermined paths. The capabilities and limitations of the approach are evaluated for a wide range of ion-conducting solids. An integrated simple kinetic Monte Carlo simulation provides rough (but less reliable) predictions of the absolute conductivity at a given temperature. The automated adaptation of the force field to the composition and charge distribution in the simulated material allows for a high transferability of the force field within a wide range of Lewis acid–Lewis base-type ionic inorganic compounds as necessary for high-throughput screening. While the transferability and precision will not reach the same levels as in DFT simulations, the fact that the computational cost is several orders of magnitude lower allows the application of the approach not only to pre-screen databases of simple structure prototypes but also to structure models of complex disordered or amorphous phases, and provides a path to expand the analysis to charge transfer across interfaces that would be difficult to cover by ab initio methods.

scholarly journals High-Throughput Screening of Metal–organic Frameworks for Macroscale Heteroepitaxial Alignment

2018 ◽  
Author(s):  
Andrew Tarzia ◽  
Masahide Takahashi ◽  
Paolo Falcaro ◽  
Aaron Thornton ◽  
Christian Doonan ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Defect Formation ◽  
Metal Organic Frameworks ◽  
Rectangular Lattice ◽  
Copper Hydroxide ◽  
Computational Screening ◽  
Screening Algorithm ◽  
Wide Range ◽  
Metal Organic

The ability to align porous metal–organic frameworks (MOFs) on substrate surfaces on a macroscopic scale is a vital step towards integrating MOFs into functional devices. But macroscale surface alignment of MOF crystals has only been demonstrated in a few cases. To accelerate the materials discovery process, we have developed a high-throughput computational screening algorithm to identify MOFs that are likely to undergo macroscale aligned heterepitaxial growth on a substrate. Screening of thousands of MOF structures by this process can be achieved in a few days on a desktop workstation. The algorithm filters MOFs based on surface chemical compatibility, lattice matching with the substrate, and interfacial bonding. Our method uses a simple new computationally efficient measure of the interfacial energy that considers both bond and defect formation at the interface. Furthermore, we show that this novel descriptor is a better predictor of aligned heteroepitaxial growth than other established interface descriptors, by testing our screening algorithm on a sample set of copper MOFs that have been grown heteroepitaxially on a copper hydroxide surface. Application of the screening process to several MOF databases reveals that the top candidates for aligned growth on copper hydroxide comprise mostly MOFs with rectangular lattice symmetry in the plane of the substrate. This result indicates a substrate-directing effect that could be exploited in targeted synthetic strategies. We also identify that MOFs likely to form aligned heterostructures have broad distributions of in-plane pore sizes and anisotropies. Accordingly, this suggests that aligned MOF thin films with a wide range of properties may be experimentally accessible.

scholarly journals High-Throughput Screening of Metal–organic Frameworks for Macroscale Heteroepitaxial Alignment

2018 ◽  
Author(s):  
Andrew Tarzia ◽  
Masahide Takahashi ◽  
Paolo Falcaro ◽  
Aaron Thornton ◽  
Christian Doonan ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Defect Formation ◽  
Metal Organic Frameworks ◽  
Rectangular Lattice ◽  
Copper Hydroxide ◽  
Computational Screening ◽  
Screening Algorithm ◽  
Wide Range ◽  
Metal Organic

The ability to align porous metal–organic frameworks (MOFs) on substrate surfaces on a macroscopic scale is a vital step towards integrating MOFs into functional devices. But macroscale surface alignment of MOF crystals has only been demonstrated in a few cases. To accelerate the materials discovery process, we have developed a high-throughput computational screening algorithm to identify MOFs that are likely to undergo macroscale aligned heterepitaxial growth on a substrate. Screening of thousands of MOF structures by this process can be achieved in a few days on a desktop workstation. The algorithm filters MOFs based on surface chemical compatibility, lattice matching with the substrate, and interfacial bonding. Our method uses a simple new computationally efficient measure of the interfacial energy that considers both bond and defect formation at the interface. Furthermore, we show that this novel descriptor is a better predictor of aligned heteroepitaxial growth than other established interface descriptors, by testing our screening algorithm on a sample set of copper MOFs that have been grown heteroepitaxially on a copper hydroxide surface. Application of the screening process to several MOF databases reveals that the top candidates for aligned growth on copper hydroxide comprise mostly MOFs with rectangular lattice symmetry in the plane of the substrate. This result indicates a substrate-directing effect that could be exploited in targeted synthetic strategies. We also identify that MOFs likely to form aligned heterostructures have broad distributions of in-plane pore sizes and anisotropies. Accordingly, this suggests that aligned MOF thin films with a wide range of properties may be experimentally accessible.

scholarly journals Data-Driven Approaches Can Overcome Limitations in Multireference Diagnostics

2020 ◽  
Author(s):  
Chenru Duan ◽  
Fang Liu ◽  
Aditya Nandy ◽  
Heather Kulik
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Density Functional ◽  
Correlation Energy ◽  
Low Cost ◽  
Small Data ◽  
Data Sets ◽  
Strongly Correlated ◽  
Heavy Atoms ◽  
Computational Screening

High-throughput computational screening typically employs methods (i.e., density functional theory or DFT) that can fail to describe challenging molecules, such as those with strongly correlated electronic structure. In such cases, multireference (MR) correlated wavefunction theory (WFT) would be the appropriate choice but remains more challenging to carry out and automate than single-reference (SR) WFT or DFT. Numerous diagnostics have been proposed for identifying when MR character is likely to have an effect on the predictive power of SR calculations, but conflicting conclusions about diagnostic performance have been reached on small data sets. We compute 15 MR diagnostics, ranging from affordable DFT-based to more costly MR-WFT-based diagnostics, on a set of 3,165 equilibrium and distorted small organic molecules containing up to six heavy atoms. Conflicting MR character assignments and low pairwise linear correlations among diagnostics are also observed over this set. We evaluate the ability of existing diagnostics to predict the percent recovery of the correlation energy, %<i>E</i><sub>corr</sub>. None of the DFT-based diagnostics are nearly as predictive of %<i>E</i><sub>corr</sub> as the best WFT-based diagnostics. To overcome the limitation of this cost–accuracy trade-off, we develop machine learning (ML, i.e., kernel ridge regression) models to predict WFT-based diagnostics from a combination of DFT-based diagnostics and a new, size-independent 3D geometric representation. The ML-predicted diagnostics correlate as well with MR effects as their computed (i.e., with WFT) values, significantly improving over the DFT-based diagnostics on which the models were trained. These ML models thus provide a promising approach to improve upon DFT-based diagnostic accuracy while remaining suitably low cost for high-throughput screening.

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