A high-throughput method for fitting dose–response curves using Microsoft Excel

2007 ◽  
Vol 360 (2) ◽  
pp. 309-311 ◽  
Author(s):  
Jonathan D. Parsons
Keyword(s):  
High Throughput ◽  
Dose Response ◽  
Response Curves ◽  
Microsoft Excel ◽  
High Throughput Method ◽  
Dose Response Curves

scholarly journals Drug combination sensitivity scoring facilitates the discovery of synergistic and efficacious drug combinations in cancer

2019 ◽  
Author(s):  
Alina Malyutina ◽  
Muntasir Mamun Majumder ◽  
Wenyu Wang ◽  
Alberto Pessia ◽  
Caroline A. Heckman ◽  
...  
Keyword(s):  
Cancer Cells ◽  
High Throughput ◽  
Dose Response ◽  
Drug Combination ◽  
Drug Combinations ◽  
Support Vector ◽  
Computational Techniques ◽  
Response Curves ◽  
Sensitivity Score ◽  
Dose Response Curves

AbstractHigh-throughput drug sensitivity screening has been utilized for facilitating the discovery of drug combinations in cancer. Many existing studies adopted a dose-response matrix design, aiming for the characterization of drug combination sensitivity and synergy. However, there is lack of consensus on the definition of sensitivity and synergy, leading to the use of different mathematical models that do not necessarily agree with each other. We proposed a cross design to enable a more cost-effective testing of sensitivity and synergy for a drug pair. We developed a drug combination sensitivity score (CSS) to summarize the drug combination dose-response curves. Using a high-throughput drug combination dataset, we showed that the CSS is highly reproducible among the replicates. With machine learning approaches such as Elastic Net, Random Forests and Support Vector Machines, the CSS can also be predicted with high accuracy. Furthermore, we defined a synergy score based on the difference between the drug combination and the single drug dose-response curves. We showed that the CSS-based synergy score is able to detect true synergistic and antagonistic drug combinations. The cross drug combination design coupled with the CSS scoring facilitated the evaluation of drug combination sensitivity and synergy using the same scale, with minimal experimental material that is required. Our approach could be utilized as an efficient pipeline for improving the discovery rate in high-throughput drug combination screening. The R scripts for calculating and predicting CSS are available at https://github.com/amalyutina/CSS.Author summaryBeing a complex disease, cancer is one of the main death causes worldwide. Although new treatment strategies have been achieved with cancers, they still have limited efficacy. Even when there is an initial treatment response, cancer cells can develop drug resistance thus cause disease recurrence. To achieve more effective and safe therapies to treat cancer, patients critically need multi-targeted drug combinations that will kill cancer cells at reduced dosages and thereby avoid side effects that are often associated with the standard treatment. However, the increasing number of possible drug combinations makes a pure experimental approach unfeasible, even with automated drug screening instruments. Therefore, we have proposed a new experimental set up to get the drug combination sensitivity data cost-efficiently and developed a score to quantify the efficiency of the drug combination, called drug combination sensitivity score (CSS). Using public datasets, we have shown that the CSS robustness and its highly predictive nature with an accuracy comparable to the experimental replicates. We have also defined a CSS-based synergy score as a metric of drug interaction and justified its relevance. Thus, we expect the proposed computational techniques to be easily applicable and beneficial in the field of drug combination discovery.

scholarly journals Validation of a High-Throughput, Automated Electrophysiology Platform for the Screening of Nicotinic Agonists and Antagonists

2012 ◽  
Vol 18 (1) ◽  
pp. 116-127 ◽  
Author(s):  
John D. Graef ◽  
Lisa C. Benson ◽  
Serguei S. Sidach ◽  
Haiyang Wei ◽  
Patrick M. Lippiello ◽  
...  
Keyword(s):  
High Throughput ◽  
Dose Response ◽  
Throughput Capacity ◽  
Response Curves ◽  
Uncompetitive Inhibition ◽  
Compound Screening ◽  
Methodological Approaches ◽  
Dose Response Curves ◽  
Automated Platform ◽  
Electrophysiological Methods

High-throughput compound screening using electrophysiology-based assays represents an important tool for biomedical research and drug discovery programs. The recent development and availability of devices capable of performing high-throughput electrophysiology-based screening have brought the need to validate these tools by producing data that are consistent with results obtained with conventional electrophysiological methods. In this study, we compared the response properties of hα3β4 and hα4β2 nicotinic receptors to their endogenous ligand acetylcholine (ACh) using three separate electrophysiology platforms: Dynaflow (low-throughput, manual system), PatchXpress 7000A (medium-throughput automated platform), and IonWorks Barracuda (high-throughput automated platform). We found that despite the differences in methodological approaches between these technologies, the EC50 values from the ACh dose-response curves were consistent between all three platforms. In addition, we have validated the IonWorks Barracuda for both competitive and uncompetitive inhibition assays by using the competitive nicotinic antagonist dihydro-beta-erythroidin (DHβE) and uncompetitive nicotinic antagonist mecamylamine. Furthermore, we have demonstrated the utility of a custom-written algorithm for generating dose-response curves from multiple extrapolated current metrics that allows for discriminating between competitive and uncompetitive inhibition while maintaining high-throughput capacity. This study provides validation of the consistency of results using low-, medium-, and high-throughput electrophysiology platforms and supports their use for screening nicotinic compounds.

Dose-response Curves in the Fibrin Plate Assay Fibrinolytic Activity of Proteases

1974 ◽  
Vol 32 (02/03) ◽  
pp. 356-365 ◽  
Author(s):  
F Haverkate ◽  
D. W Traas
Keyword(s):  
Dose Response ◽  
Fibrinolytic Activity ◽  
Enzyme Concentration ◽  
Response Curves ◽  
Agar Gel ◽  
Porcine Tissue ◽  
Plate Assay ◽  
Fibrin Plate ◽  
Different Types ◽  
Dose Response Curves

SummaryIn the fibrin plate assay different types of relationships between the dose of applied proteolytic enzyme and the response have been previously reported. This study was undertaken to determine whether a generally valid relationship might exist.Trypsin, chymotrypsin, papain, the plasminogen activator urokinase and all of the microbial proteases investigated, including brinase gave a linear relationship between the logarithm of the enzyme concentration and the diameter of the circular lysed zone. A similar linearity of dose-response curves has frequently been found by investigators who used enzyme plate assays with substrates different from fibrin incorporated in an agar gel. Consequently, it seems that this linearity of dose-response curves is generally valid for the fibrin plate assay as well as for other enzyme plate bioassays.Both human plasmin and porcine tissue activator of plasminogen showed deviations from linearity of semi-logarithmic dose-response curves in the fibrin plate assay.

BIOASSAY OF THYROID HORMONE USING HYPOTHYROID TADPOLES

1962 ◽  
Vol 41 (1) ◽  
pp. 143-153 ◽  
Author(s):  
U. Henriques
Keyword(s):  
Thyroid Hormone ◽  
Dose Response ◽  
Developmental Rate ◽  
Response Curves ◽  
Sodium Salts ◽  
Dose Response Curves

ABSTRACT A bioassay of thyroid hormone has been developed using Xenopus larvae made hypothyroid by the administration of thiourea. Only tadpoles of uniform developmental rate were used. Thiourea was given just before the metamorphotic climax in concentrations that produced neoteni in an early metamorphotic stage. During maintained thiourea neotoni, 1-thyroxine and 1-triiodothyronine were added as sodium salts to the water for three days and at the end of one week the stage of metamorphosis produced was determined. In this way identical dose-response curves were obtained for the two compounds. No qualitative differences between their effects were noted except that triiodothyronine seemed more toxic than thyroxine in equivalent doses. Triiodothyronine was found to be 7–12 times as active as thyroxine.

Dose Response Curves for Microwave Ablation in the Cardioplegia-Arrested Porcine Heart

2005 ◽  
Vol 8 (4) ◽  
pp. E269-E274 ◽  
Author(s):  
Sydney L. Gaynor ◽  
Gregory D. Byrd ◽  
Michael D. Diodato ◽  
Yosuke Ishii ◽  
Anson M. Lee ◽  
...  
Keyword(s):  
Dose Response ◽  
Microwave Ablation ◽  
Response Curves ◽  
Porcine Heart ◽  
Dose Response Curves

Modeling Nonmonotonic Dose-Response Curves

2001 ◽  
Author(s):  
Quinton J. Nottingham ◽  
Jeffrey B. Birch ◽  
Barry A. Bodt
Keyword(s):  
Dose Response ◽  
Response Curves ◽  
Dose Response Curves

scholarly journals Comment Concerning the Effects of Light Intensity on Melatonin Suppression in the Review “Light Modulation of Human Clocks, Wake, and Sleep” by A. Prayag et al.

2021 ◽  
Vol 3 (1) ◽  
pp. 181-188
Author(s):  
Peter Bracke ◽  
Eowyn Van de Putte ◽  
Wouter R. Ryckaert
Keyword(s):  
Phase Shift ◽  
Light Intensity ◽  
Dose Response ◽  
Light Modulation ◽  
Response Curves ◽  
Practical Applications ◽  
Circadian Phase ◽  
Melatonin Suppression ◽  
Dose Response Curves ◽  
Circadian Phase Shift

Dose-response curves for circadian phase shift and melatonin suppression in relation to white or monochromatic nighttime illumination can be scaled to melanopic weighed illumination for normally constricted pupils, which makes them easier to interpret and compare. This is helpful for a practical applications.

scholarly journals Nonparametric estimation of population average dose-response curves using entropy balancing weights for continuous exposures

2021 ◽  
Vol 21 (1) ◽  
pp. 69-110 ◽  
Author(s):  
Brian G. Vegetabile ◽  
Beth Ann Griffin ◽  
Donna L. Coffman ◽  
Matthew Cefalu ◽  
Michael W. Robbins ◽  
...  
Keyword(s):  
Nonparametric Estimation ◽  
Dose Response ◽  
Average Dose ◽  
Response Curves ◽  
Entropy Balancing ◽  
Population Average ◽  
Dose Response Curves
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