scholarly journals High-Throughput Measurement and Machine Learning-Based Prediction of Collision Cross Sections for Drugs and Drug Metabolites

2021 ◽  
Author(s):  
Dylan H. Ross ◽  
Ryan P. Seguin ◽  
Allison M. Krinsky ◽  
Libin Xu
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
High Throughput ◽  
Large Scale ◽  
Collision Cross Section ◽  
Physical Property ◽  
Metabolite Identification ◽  
Drug Metabolites ◽  
Drug Metabolite

Drug metabolite identification is a bottleneck of drug metabolism studies. Ion mobility-mass spectrometry (IM-MS) enables the measurement of collision cross section (CCS), a unique physical property related to an ion's gas-phase size and shape, which can be used to increase the confidence in the identification of unknowns. A current limitation to the application of IM-MS to the identification of drug metabolites is the lack of reference CCS values. In this work, we present the production of a large-scale database of drug and drug metabolite CCS values, assembled using high-throughput in vitro drug metabolite generation and a rapid IM-MS analysis with automated data processing. Subsequently, we used this database to train a machine learning-based CCS prediction model, employing a combination of conventional 2D molecular descriptors and novel 3D descriptors. This novel prediction model enables the prediction of different CCS values for different protomers, conformers, and positional isomers for the first time.

scholarly journals High Throughput Phenotypic Screening of The Human Spermatozoon

Reproduction ◽  
2021 ◽  
Author(s):  
Zoe Claire Johnston ◽  
Franz S Gruber ◽  
Sean Brown ◽  
Neil R Norcross ◽  
Jason R Swedlow ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Large Scale ◽  
Hormonal Contraceptives ◽  
Diagnostic Tools ◽  
Sperm Function ◽  
Phenotypic Screening ◽  
Environmental Chemical ◽  
Potential Applications

Despite recent advances in male reproductive health research, there remain many elements of male (in)fertility where our understanding is incomplete. Consequently, diagnostic tools and treatments for men with sperm dysfunction, other than medically assisted reproduction, are limited. On the other hand, the gaps in our knowledge of the mechanisms which underpin sperm function have hampered the development of male non-hormonal contraceptives. The study of mature spermatozoa is inherently difficult. They are a unique and highly specialised cell type which does not actively transcribe or translate proteins and cannot be cultured for long periods of time or matured in vitro. One, large scale, approach to both increasing understanding of sperm function, and the discovery and development of compounds that can modulate sperm function, is to directly observe responses to compounds with phenotypic screening techniques. These target agnostic approaches can be developed into high-throughput screening platforms with the potential to drastically increase advances in the field. Here we discuss the rationale and development of high-throughput phenotypic screening platforms for mature human spermatozoa, and the multiple potential applications these present, as well as the current limitations and leaps in our understanding and capabilities needed to overcome them. Further development and use of these technologies could lead to the identification of compounds which positively or negatively affect sperm cell motility or function, or novel platforms for toxicology or environmental chemical testing among other applications. Ultimately, each of these potential applications is also likely to increase understanding within the field of sperm biology.

scholarly journals Machine Learning-Based Prediction Model for Papillary Thyroid Carcinoma Recurrence

2020 ◽  
Author(s):  
Young Min Park ◽  
Byung-Joo Lee
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Tumor Size ◽  
Large Scale ◽  
Prediction Models ◽  
Prognostic Significance ◽  
Disease Recurrence ◽  
Machine Learning Techniques ◽  
Papillary Thyroid ◽  
Recurrence Prediction

Abstract Background: This study analyzed the prognostic significance of nodal factors, including the number of metastatic LNs and LNR, in patients with PTC, and attempted to construct a disease recurrence prediction model using machine learning techniques.Methods: We retrospectively analyzed clinico-pathologic data from 1040 patients diagnosed with papillary thyroid cancer between 2003 and 2009. Results: We analyzed clinico-pathologic factors related to recurrence through logistic regression analysis. Among the factors that we included, only sex and tumor size were significantly correlated with disease recurrence. Parameters such as age, sex, tumor size, tumor multiplicity, ETE, ENE, pT, pN, ipsilateral central LN metastasis, contralateral central LNs metastasis, number of metastatic LNs, and LNR were input for construction of a machine learning prediction model. The performance of five machine learning models related to recurrence prediction was compared based on accuracy. The Decision Tree model showed the best accuracy at 95%, and the lightGBM and stacking model together showed 93% accuracy. Conclusions: We confirmed that all machine learning prediction models showed an accuracy of 90% or more for predicting disease recurrence in PTC. Large-scale multicenter clinical studies should be performed to improve the performance of our prediction models and verify their clinical effectiveness.

High-Throughput Electrophysiology Screen Revealed Cardiotoxicity of Strychnine by Selectively Targeting hERG Channel

2018 ◽  
Vol 46 (08) ◽  
pp. 1825-1840 ◽  
Author(s):  
Taiyi Wang ◽  
Xiaonan Chen ◽  
Jiahui Yu ◽  
Qunqun Du ◽  
Jie Zhu ◽  
...  
Keyword(s):  
Herbal Medicine ◽  
Patch Clamp ◽  
High Throughput ◽  
Chinese Herbal Medicine ◽  
Large Scale ◽  
Cardiac Toxicity ◽  
Herg Channel ◽  
Chinese Herbal ◽  
Cell Vitality

Although the efficacy and the health care advantages of Chinese herbal medicine (CHM) have become increasingly recognized worldwide, the potential side effects and toxicity still restrict its broader application. This study established and applied an integrated platform anchored on automatic patch clamp system to screen and evaluate a collection of CHM extracts, compositions and monomeric compounds for in vitro cardiac toxicity. Of 1036 CHM samples screened, 2.79% significantly inhibited hERG channel activity. Among them, Strychnine was identified for the first time as a potent hERG inhibitor with an IC[Formula: see text] of [Formula: see text]M in comparison to that of Dofetilide at [Formula: see text]M and Quinidine at [Formula: see text]M. Langendorff-perfusion experiments confirmed that strychnine increased QT interphase from [Formula: see text][Formula: see text]ms to [Formula: see text][Formula: see text]ms and decreased heart rates from [Formula: see text][Formula: see text]bmp to [Formula: see text][Formula: see text]bmp in isolated rat hearts. The cardiac toxicity effect of strychnine appears to be specific to hERG channel since an in vitro multiplex imaging analysis showed that it did not affect cellular phenotypes such as cell vitality, nucleus area, mitochondria mass and function, nor intracellular calcium in rat primary myocytes. This integrated high-throughput hERG patch clamp and high-content multi-parameter imaging cardiac toxicity screen approach should be useful for large-scale preclinical evaluation of complex Chinese herbal medicine.

scholarly journals ZF-AutoML: An Easy Machine-Learning-Based Method to Detect Anomalies in Fluorescent-Labelled Zebrafish

Inventions ◽  
2019 ◽  
Vol 4 (4) ◽  
pp. 72
Author(s):  
Ryota Sawaki ◽  
Daisuke Sato ◽  
Hiroko Nakayama ◽  
Yuki Nakagawa ◽  
Yasuhito Shimada
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
High Throughput ◽  
High Throughput Screening ◽  
Large Scale ◽  
Fluorescent Protein ◽  
Learning Program ◽  
High Throughput Analysis ◽  
Easy Method ◽  
High Fecundity

Background: Zebrafish are efficient animal models for conducting whole organism drug testing and toxicological evaluation of chemicals. They are frequently used for high-throughput screening owing to their high fecundity. Peripheral experimental equipment and analytical software are required for zebrafish screening, which need to be further developed. Machine learning has emerged as a powerful tool for large-scale image analysis and has been applied in zebrafish research as well. However, its use by individual researchers is restricted due to the cost and the procedure of machine learning for specific research purposes. Methods: We developed a simple and easy method for zebrafish image analysis, particularly fluorescent labelled ones, using the free machine learning program Google AutoML. We performed machine learning using vascular- and macrophage-Enhanced Green Fluorescent Protein (EGFP) fishes under normal and abnormal conditions (treated with anti-angiogenesis drugs or by wounding the caudal fin). Then, we tested the system using a new set of zebrafish images. Results: While machine learning can detect abnormalities in the fish in both strains with more than 95% accuracy, the learning procedure needs image pre-processing for the images of the macrophage-EGFP fishes. In addition, we developed a batch uploading software, ZF-ImageR, for Windows (.exe) and MacOS (.app) to enable high-throughput analysis using AutoML. Conclusions: We established a protocol to utilize conventional machine learning platforms for analyzing zebrafish phenotypes, which enables fluorescence-based, phenotype-driven zebrafish screening.

scholarly journals Molecular Fitness Landscapes from High-Coverage Sequence Profiling

2019 ◽  
Vol 48 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Celia Blanco ◽  
Evan Janzen ◽  
Abe Pressman ◽  
Ranajay Saha ◽  
Irene A. Chen
Keyword(s):  
High Throughput ◽  
Large Scale ◽  
High Throughput Sequencing ◽  
Fitness Landscape ◽  
Complete Sequence ◽  
Fitness Landscapes ◽  
Future Research ◽  
High Coverage

The function of fitness (or molecular activity) in the space of all possible sequences is known as the fitness landscape. Evolution is a random walk on the fitness landscape, with a bias toward climbing hills. Mapping the topography of real fitness landscapes is fundamental to understanding evolution, but previous efforts were hampered by the difficulty of obtaining large, quantitative data sets. The accessibility of high-throughput sequencing (HTS) has transformed this study, enabling large-scale enumeration of fitness for many mutants and even complete sequence spaces in some cases. We review the progress of high-throughput studies in mapping molecular fitness landscapes, both in vitro and in vivo, as well as opportunities for future research. Such studies are rapidly growing in number. HTS is expected to have a profound effect on the understanding of real molecular fitness landscapes.

scholarly journals Machine learned calibrations to high-throughput molecular excited state calculations

2022 ◽  
Author(s):  
Shomik Verma ◽  
Miguel Rivera ◽  
David O. Scanlon ◽  
Aron Walsh
Keyword(s):  
Machine Learning ◽  
Excited State ◽  
High Throughput ◽  
High Speed ◽  
Large Scale ◽  
Chemical Space ◽  
False Negative ◽  
Screening Method ◽  
Calibration Model ◽  
Linear Calibration

Understanding the excited state properties of molecules provides insights into how they interact with light. These interactions can be exploited to design compounds for photochemical applications, including enhanced spectral conversion of light to increase the efficiency of photovoltaic cells. While chemical discovery is time- and resource-intensive experimentally, computational chemistry can be used to screen large-scale databases for molecules of interest in a procedure known as high-throughput virtual screening. The first step usually involves a high-speed but low-accuracy method to screen large numbers of molecules (potentially millions) so only the best candidates are evaluated with expensive methods. However, use of a coarse first-pass screening method can potentially result in high false positive or false negative rates. Therefore, this study uses machine learning to calibrate a high-throughput technique (xTB-sTDA) against a higher accuracy one (TD-DFT). Testing the calibration model shows a ~5-fold decrease in error in-domain and a ~3-fold decrease out-of-domain. The resulting mean absolute error of ~0.14 eV is in line with previous work in machine learning calibrations and out-performs previous work in linear calibration of xTB-sTDA. We then apply the calibration model to screen a 250k molecule database and map inaccuracies of xTB-sTDA in chemical space. We also show generalizability of the workflow by calibrating against a higher-level technique (CC2), yielding a similarly low error. Overall, this work demonstrates machine learning can be used to develop a both cheap and accurate method for large-scale excited state screening, enabling accelerated molecular discovery across a variety of disciplines.

scholarly journals Bimodality of gene expression in cancer patient tumors as interpretable biomarkers for drug sensitivity

2020 ◽  
Author(s):  
Wail Ba-Alawi ◽  
Sisira Kadambat Nair ◽  
Bo Li ◽  
Anthony Mammoliti ◽  
Petr Smirnov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Human Cancer ◽  
Predictive Biomarkers ◽  
Precision Oncology ◽  
Human Cancer Cell Lines ◽  
Validation Rate ◽  
Approved Drugs ◽  
Pharmacogenomic Studies

ABSTRACTIdentifying biomarkers predictive of cancer cells’ response to drug treatment constitutes one of the main challenges in precision oncology. Recent large-scale cancer pharmacogenomic studies have boosted the research for finding predictive biomarkers by profiling thousands of human cancer cell lines at the molecular level and screening them with hundreds of approved drugs and experimental chemical compounds. Many studies have leveraged these data to build predictive models of response using various statistical and machine learning methods. However, a common challenge in these methods is the lack of interpretability as to how they make the predictions and which features were the most associated with response, hindering the clinical translation of these models. To alleviate this issue, we develop a new machine learning pipeline based on the recent LOBICO approach that explores the space of bimodally expressed genes in multiple large in vitro pharmacogenomic studies and builds multivariate, nonlinear, yet interpretable logic-based models predictive of drug response. Using our method, we used a compendium of three of the largest pharmacogenomic data sets to build robust and interpretable models for 101 drugs that span 17 drug classes with high validation rate in independent datasets.

scholarly journals Machine learning models identify molecules active against the Ebola virus in vitro

F1000Research ◽  
2017 ◽  
Vol 4 ◽  
pp. 1091 ◽  
Author(s):  
Sean Ekins ◽  
Joel S. Freundlich ◽  
Alex M. Clark ◽  
Manu Anantpadma ◽  
Robert A. Davey ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Ebola Virus ◽  
Biological Activities ◽  
European Medicines Agency ◽  
Infection Model ◽  
Learning Models ◽  
Compound Libraries ◽  
Machine Learning Models

The search for small molecule inhibitors of Ebola virus (EBOV) has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs) with anti-EBOV activity in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested in vitro and had EC50 values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors in vitro.

Cell-based models for mycotoxin screening and toxicity evaluation: an update

2014 ◽  
Vol 7 (2) ◽  
pp. 153-166 ◽  
Author(s):  
F. Cheli ◽  
E. Fusi ◽  
A. Baldi
Keyword(s):  
High Throughput ◽  
Large Scale ◽  
Mycotoxin Research ◽  
Cost Effective ◽  
Screening Tests ◽  
Test Model ◽  
Related Factors ◽  
Technical Problems ◽  
Third Dimension

This review presents the applications of cell-based models in mycotoxin research, with a focus on models for mycotoxin screening and cytotoxicity evaluation. Various cell-based models, cell and cell culture condition related factors, toxicity endpoints and culture systems as well as predictive value of cell-based bioassays are reviewed. Advantages, drawbacks and technical problems regarding set up and validation of consistent, robust, reproducible and high-throughput cell-based models are discussed. Various cell-based models have been developed and used as screening tests for mycotoxins but the data obtained are difficult to compare. However, the results highlight the potential of cell-based models as promising in vitro platforms for the initial screening and cytotoxicity evaluation of mycotoxins and as a significant analytical approach in mycotoxin research before any animal or human clinical studies. To develop cell-based models as powerful high-throughput laboratory platforms for the analysis of large numbers of samples, there are mainly two fundamental requirements that should be met, i.e. the availability of easy-to-use and, if possible, automated cell platforms and the possibility to obtain reproducible results that are comparable between laboratories. The transition from a research model to a test model still needs optimisation, standardisation, and validation of analytical protocols. The validation of a cell-based bioassay is a complex process, as several critical points, such as the choice of the cellular model, the assay procedures, and the appropriate use and interpretation of the results, must be strictly defined to ensure more consistency in the results. The development of cell-based models exploring the third dimension together with automation and miniaturisation will bring cellular platforms to a level appropriate for cost-effective and large-scale analysis in the field of mycotoxin research.

scholarly journals Machine learning models identify molecules active against the Ebola virus in vitro

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 1091 ◽  
Author(s):  
Sean Ekins ◽  
Joel S. Freundlich ◽  
Alex M. Clark ◽  
Manu Anantpadma ◽  
Robert A. Davey ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Ebola Virus ◽  
Biological Activities ◽  
European Medicines Agency ◽  
Infection Model ◽  
Learning Models ◽  
Compound Libraries ◽  
Machine Learning Models

The search for small molecule inhibitors of Ebola virus (EBOV) has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs) with anti-EBOV activity in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested in vitro and had EC50 values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors in vitro.

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