The ENDS of assumptions; an online tool for the Epistemic Nonparametric Drug-response Scoring

AbstractMotivationThe drugs sensitivity analysis is often elucidated from drug dose-response curves. These curves capture the degree of cell viability (or inhibition) over a range of induced drugs, often with parametric assumptions that are rarely validated.ResultsWe present a class of nonparametric models for the curve fitting and scoring of drug dose-responses. To allow a more objective representation of the drug sensitivity, these epistemic models devoid of any parametric assumptions attached to the linear fit, allow the parallel indexing such as IC50 and AUC. Specifically, three nonparametric models including Spline, Monotonic, and Bayesian (npS, npM, npB) and the parametric Logistic (pL) are implemented. Other indices including Maximum Effective Dose (MED) and Drug-response Span Gradient (DSG) pertinent to the npS are also provided to facilitate the interpretation of the fit. The collection of these models are implemented in an online app, standing as useful resource for drug dose-response curve fitting and analysis.AvailabilityThe ENDS is freely available online at https://irscope.shinyapps.io/ENDS/ and source codes can be obtained from https://github.com/AmiryousefiLab/ENDS.Supplementary informationSupplementary data are available at Bioinformatics and https://irscope.shinyapps.io/ENDS/[email protected]; [email protected] conceived the study and developed the models, AA and BW adopted and implemented the methods, JT provided the funding, AA, BW, MJ, and JT wrote the paper.

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THE ADDITIVITY ANTINOCICEPTIVE INTERACTIONS BETWEEN DICLOFENAC AND THE DERRIS SCANDENS EXTRACT DRUG IN MICE.

Asian Journal of Pharmaceutical and Clinical Research ◽

10.22159/ajpcr.2018.v11i1.22316 ◽

2018 ◽

Vol 11 (1) ◽

pp. 314

Author(s):

Tadsanee Punjanon

Keyword(s):

Acetic Acid ◽

Side Effects ◽

Combination Therapy ◽

Dose Response ◽

Pain Treatment ◽

Fixed Ratio ◽

Drug Dose ◽

Response Curves ◽

Interaction Index ◽

Abdominal Constriction

Objective: Combination therapy is a valid approach in pain treatment, in which a reduction of doses could reduce side effects and still achieve optimal analgesia. The objective was to determine the effects of coadministered diclofenac and the Derris scandens extract drug.Methods: Acetic acid-induced abdominal constriction test in mice was used to determine the type of interaction between components. The effective dose that produced 50% antinociception (ED50) was calculated from the log dose-response curves of fixed ratio combinations of diclofenac with the D. scandens extract drug. The ED50 was compared to the theoretical additive ED50 calculated from the ED50 of diclofenac and of the D. scandens extract drug alone.Results: Diclofenac and the D. scandens extract drug dose‐dependently and significantly reduced the abdominal writhing. The combination was the additive effect, the experimental ED50 being smaller than the theoretically calculated ED50. Interaction index of the combination was 0.89.Conclusion: The present study demonstrates the additivity antinociceptive interactions between diclofenac and the D. scandens extract drug and may be used as a combination analgesic in the treatment of pain conditions.

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Pharmacological curve fitting to analyze cutaneous adrenergic responses

Journal of Applied Physiology ◽

10.1152/japplphysiol.00780.2011 ◽

2011 ◽

Vol 111 (6) ◽

pp. 1703-1709 ◽

Cited By ~ 23

Author(s):

Megan M. Wenner ◽

Thad E. Wilson ◽

Scott L. Davis ◽

Nina S. Stachenfeld

Keyword(s):

Dose Response ◽

Curve Fitting ◽

Repeated Measures ◽

Nonlinear Modeling ◽

Response Curves ◽

Data Set ◽

Sigmoidal Model ◽

Dose Response Curves ◽

Cutaneous Vasoconstriction

Although dose-response curves are commonly used to describe in vivo cutaneous α-adrenergic responses, modeling parameters and analyses methods are not consistent across studies. The goal of the present investigation was to compare three analysis methods for in vivo cutaneous vasoconstriction studies using one reference data set. Eight women (22 ± 1 yr, 24 ± 1 kg/m2) were instrumented with three cutaneous microdialysis probes for progressive norepinephrine (NE) infusions (1 × 10−8, 1 × 10−6, 1 × 10−5, 1 × 10−4, and 1 × 10−3 logM). NE was infused alone, co-infused with NG-monomethyl-l-arginine (l-NMMA, 10 mM) or Ketorolac tromethamine (KETO, 10 mM). For each probe, dose-response curves were generated using three commonly reported analyses methods: 1) nonlinear modeling without data manipulation, 2) nonlinear modeling with data normalization and constraints, and 3) percent change from baseline without modeling. Not all data conformed to sigmoidal dose-response curves using analysis 1, whereas all subjects' curves were modeled using analysis 2. When analyzing only curves that fit the sigmoidal model, NE + KETO induced a leftward shift in ED50 compared with NE alone with analyses 1 and 2 ( F test, P < 0.05) but only tended to shift the response leftward with analysis 3 (repeated-measures ANOVA, P = 0.08). Neither maximal vasoconstrictor capacity (Emax) in analysis 1 nor %change CVC change from baseline in analysis 3 were altered by blocking agents. In conclusion, although the overall detection of curve shifts and interpretation was similar between the two modeling methods of curve fitting, analysis 2 produced more sigmoidal curves.

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Statistical Quality Control and Routine Data Processing for Radioimmunoassays and Immunoradiometric Assays

Clinical Chemistry ◽

10.1093/clinchem/20.10.1255 ◽

1974 ◽

Vol 20 (10) ◽

pp. 1255-1270 ◽

Cited By ~ 641

Author(s):

David Rodbard

Keyword(s):

Quality Control ◽

Data Processing ◽

Dose Response ◽

Curve Fitting ◽

Routine Data ◽

Statistical Quality Control ◽

Graphical Display ◽

Response Curves ◽

Statistical Quality ◽

Ideal System

Abstract Numerous methods are available for the graphical display of radioimmunoassay dose—response curves, for curve-fitting and dose interpolation, for statistical quality control, and for automation and computerization of data processing. The relative merits of these approaches are discussed. Minimal requirements for radioimmunoassay data-processing systems are presented. The features of an "ideal" system are discussed.

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Analysis of dose-response curves and calculation of agonist dissociation constants using a weighted nonlinear curve fitting program

Journal of Pharmacological Methods ◽

10.1016/0160-5402(83)90017-7 ◽

1983 ◽

Vol 10 (4) ◽

pp. 231-241 ◽

Cited By ~ 34

Author(s):

G.A. McPherson ◽

P. Molenaar ◽

C. Raper ◽

E. Malta

Keyword(s):

Dose Response ◽

Curve Fitting ◽

Dissociation Constants ◽

Response Curves ◽

Nonlinear Curve Fitting ◽

Dose Response Curves ◽

Nonlinear Curve

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A regularized functional regression model enabling transcriptome-wide dosage-dependent association study of cancer drug response

10.1101/2020.06.18.158907 ◽

2020 ◽

Author(s):

Evanthia Koukouli ◽

Dennis Wang ◽

Frank Dondelinger ◽

Juhyun Park

Keyword(s):

Gene Expression ◽

Regression Model ◽

Cell Line ◽

Cell Lines ◽

Dose Response ◽

Drug Response ◽

Drug Sensitivity ◽

Cancer Drug ◽

Functional Regression ◽

Response Curves

AbstractCancer treatments can be highly toxic and frequently only a subset of the patient population will benefit from a given treatment. Tumour genetic makeup plays an important role in cancer drug sensitivity. We suspect that gene expression markers could be used as a decision aid for treatment selection or dosage tuning. Using in vitro cancer cell line dose-response and gene expression data from the Genomics of Drug Sensitivity in Cancer (GDSC) project, we build a dose-varying regression model. Unlike existing approaches, this allows us to estimate dosage-dependent associations with gene expression. We include the transcriptomic profiles as dose-invariant covariates into the regression model and assume that their effect varies smoothly over the dosage levels. A two-stage variable selection algorithm (variable screening followed by penalised regression) is used to identify genetic factors that are associated with drug response over the varying dosages. We evaluate the effectiveness of our method using simulation studies focusing on the choice of tuning parameters and cross-validation for predictive accuracy assessment. We further apply the model to data from five BRAF targeted compounds applied to different cancer cell lines under different dosage levels. We highlight the dosage-dependent dynamics of the associations between the selected genes and drug response, and we perform pathway enrichment analysis to show that the selected genes play an important role in pathways related to tumourgenesis and DNA damage response.Author SummaryTumour cell lines allow scientists to test anticancer drugs in a laboratory environment. Cells are exposed to the drug in increasing concentrations, and the drug response, or amount of surviving cells, is measured. Generally, drug response is summarized via a single number such as the concentration at which 50% of the cells have died (IC50). To avoid relying on such summary measures, we adopted a functional regression approach that takes the dose-response curves as inputs, and uses them to find biomarkers of drug response. One major advantage of our approach is that it describes how the effect of a biomarker on the drug response changes with the drug dosage. This is useful for determining optimal treatment dosages and predicting drug response curves for unseen drug-cell line combinations. Our method scales to large numbers of biomarkers by using regularisation and, in contrast with existing literature, selects the most informative genes by accounting for drug response at untested dosages. We demonstrate its value using data from the Genomics of Drug Sensitivity in Cancer project to identify genes whose expression is associated with drug response. We show that the selected genes recapitulate prior biological knowledge, and belong to known cancer pathways.

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HierCC: A multi-level clustering scheme for population assignments based on core genome MLST

10.1101/2020.11.25.397539 ◽

2020 ◽

Author(s):

Zhemin Zhou ◽

Jane Charlesworth ◽

Mark Achtman

Keyword(s):

Disease Surveillance ◽

Large Scale ◽

Core Genome ◽

Supplementary Information ◽

Source Codes ◽

Link Type ◽

Scalable Clustering ◽

Population Structures ◽

Multi Level ◽

Level Cluster

AbstractMotivationRoutine infectious disease surveillance is increasingly based on large-scale whole genome sequencing databases. Real-time surveillance would benefit from immediate assignments of each genome assembly to hierarchical population structures. Here we present HierCC, a scalable clustering scheme based on core genome multi-locus typing that allows incremental, static, multi-level cluster assignments of genomes. We also present HCCeval, which identifies optimal thresholds for assigning genomes to cohesive HierCC clusters. HierCC was implemented in EnteroBase in 2018, and has since genotyped >400,000 genomes from Salmonella, Escherichia, Yersinia and Clostridioides.AvailabilityImplementation: http://enterobase.warwick.ac.uk/ and Source codes: https://github.com/zheminzhou/[email protected] informationSupplementary data are available at Bioinformatics online.

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DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks

10.1101/351601 ◽

2018 ◽

Cited By ~ 7

Author(s):

Mostafa Karimi ◽

Di Wu ◽

Zhangyang Wang ◽

Yang shen

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Supplementary Information ◽

Seamless Integration ◽

Source Codes ◽

Link Type ◽

Protein Affinity ◽

Protein Classes ◽

Deep Learning Model

AbstractMotivationDrug discovery demands rapid quantification of compound-protein interaction (CPI). However, there is a lack of methods that can predict compound-protein affinity from sequences alone with high applicability, accuracy, and interpretability.ResultsWe present a seamless integration of domain knowledges and learning-based approaches. Under novel representations of structurally-annotatedprotein sequences, a semi-supervised deep learning model that unifies recurrent and convolutional neural networks has been proposed to exploit both unlabeled and labeled data, for jointly encoding molecular representations and predicting affinities. Our representations and models outperform conventional options in achieving relative error in IC50 within 5-fold for test cases and 20-fold for protein classes not included for training. Performances for new protein classes with few labeled data are further improved by transfer learning. Furthermore, separate and joint attention mechanisms are developed and embedded to our model to add to its interpretability, as illustrated in case studies for predicting and explaining selective drug-target interactions. Lastly, alternative representations using protein sequences or compound graphs and a unified RNN/GCNN-CNN model using graph CNN (GCNN) are also explored to reveal algorithmic challenges ahead.AvailabilityData and source codes are available at https://github.com/Shen-Lab/[email protected] informationSupplementary data are available at http://shen-lab.github.io/deep-affinity-bioinf18-supp-rev.pdf.

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Revisiting inconsistency in large pharmacogenomic studies

10.1101/026153 ◽

2015 ◽

Cited By ~ 5

Author(s):

Zhaleh Safikhani ◽

Mark Freeman ◽

Petr Smirnov ◽

Nehme El-Hachem ◽

Adrian She ◽

...

Keyword(s):

Gene Expression ◽

Cell Lines ◽

Dose Response ◽

Drug Sensitivity ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Drug Dose ◽

Response Curves ◽

Sensitivity Data ◽

Pharmacogenomic Studies

Background: In 2012, two large pharmacogenomic studies, the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE), were published, each reported gene expression data and measures of drug response for a large number of drugs and hundreds of cell lines. In 2013, we published a comparative analysis that reported gene expression profiles for the 471 cell lines profiled in both studies and dose response measurements for the 15 drugs characterized in the common cell lines by both studies. While we found good concordance in gene expression profiles, there was substantial inconsistency in the drug responses reported by the GDSC and CCLE projects. Our paper was widely discussed and we received extensive feedback on the comparisons that we performed. This feedback, along with the release of new data, prompted us to revisit our initial analysis. Here we present a new analysis using these expanded data in which we address the most significant suggestions for improvements on our published analysis: that drugs with different response characteristics should have been treated differently, that targeted therapies and broad cytotoxic drugs should have been treated differently in assessing consistency, that consistency of both molecular profiles and drug sensitivity measurements should both be compared across cell lines to accurately assess differences in the studies, that we missed some biomarkers that are consistent between studies, and that the software analysis tools we provided with our analysis should have been easier to run, particularly as the GDSC and CCLE released additional data. Methods: For each drug, we used published sensitivity data from the GDSC and CCLE to separately estimate drug dose-response curves. We then used two statistics, the area between drug dose-response curves (ABC) and the Matthews correlation coefficient (MCC), to robustly estimate the consistency of continuous and discrete drug sensitivity measures, respectively. We also used recently released RNA-seq data together with previously published gene expression microarray data to assess inter-platform reproducibility of cell line gene expression profiles. Results: This re-analysis supports our previous finding that gene expression data are significantly more consistent than drug sensitivity measurements. The use of new statistics to assess data consistency allowed us to identify two broad effect drugs -- 17-AAG and PD-0332901 -- and three targeted drugs -- PLX4720, nilotinib and crizotinib -- with moderate to good consistency in drug sensitivity data between GDSC and CCLE. Not enough sensitive cell lines were screened in both studies to robustly assess consistency for three other targeted drugs, PHA-665752, erlotinib, and sorafenib. Concurring with our published results, we found evidence of inconsistencies in pharmacological phenotypes for the remaining eight drugs. Further, to discover "consistency" between studies required the use of multiple statistics and the selection of specific measures on a case-by-case basis. Conclusion: Our results reaffirm our initial findings of an inconsistency in drug sensitivity measures for eight of fifteen drugs screened both in GDSC and CCLE, irrespective of which statistical metric was used to assess correlation. Taken together, our findings suggest that the phenotypic data on drug response in the GDSC and CCLE continue to present challenges for robust biomarker discovery. This re-analysis provides additional support for the argument that experimental standardization and validation of pharmacogenomic response will be necessary to advance the broad use of large pharmacogenomic screens.

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