TY - JOUR

T1 - Measuring and statistically testing the size of the effect of a chemical compound on a continuous in-vitro pharmacological response through a new statistical model of response detection limit

AU - Diaz, Francisco J.

AU - McDonald, Peter R.

AU - Pinter, Abraham

AU - Chaguturu, Rathnam

N1 - Funding Information:
The models were developed during Dr. Diaz part-time work (20% of his time) at the High Throughput Screening Laboratory of the University of Kansas, Lawrence, KS. His salary for this work was provided by the University of Kansas Endowment Association. The neutralization data reported in this article were produced by Drs. Pinter, Chaguturu, and McDonald in the context of NIH grant # R21NS067633, which was awarded to Dr. Pinter; Dr. Diaz was not involved in the planning or conduction of the biochemical experiments producing these data, and did not receive any payment from this NIH grant or from Drs. Pinter, Chaguturu, or McDonald. The contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.
Publisher Copyright:
Copyright © Taylor & Francis Group, LLC.

PY - 2015/7/4

Y1 - 2015/7/4

N2 - Biomolecular screening research frequently searches for the chemical compounds that are most likely to make a biochemical or cell-based assay system produce a strong continuous response. Several doses are tested with each compound and it is assumed that, if there is a dose-response relationship, the relationship follows a monotonic curve, usually a version of the median-effect equation. However, the null hypothesis of no relationship cannot be statistically tested using this equation. We used a linearized version of this equation to define a measure of pharmacological effect size, and use this measure to rank the investigated compounds in order of their overall capability to produce strong responses. The null hypothesis that none of the examined doses of a particular compound produced a strong response can be tested with this approach. The proposed approach is based on a new statistical model of the important concept of response detection limit, a concept that is usually neglected in the analysis of dose-response data with continuous responses. The methodology is illustrated with data from a study searching for compounds that neutralize the infection by a human immunodeficiency virus of brain glioblastoma cells.

AB - Biomolecular screening research frequently searches for the chemical compounds that are most likely to make a biochemical or cell-based assay system produce a strong continuous response. Several doses are tested with each compound and it is assumed that, if there is a dose-response relationship, the relationship follows a monotonic curve, usually a version of the median-effect equation. However, the null hypothesis of no relationship cannot be statistically tested using this equation. We used a linearized version of this equation to define a measure of pharmacological effect size, and use this measure to rank the investigated compounds in order of their overall capability to produce strong responses. The null hypothesis that none of the examined doses of a particular compound produced a strong response can be tested with this approach. The proposed approach is based on a new statistical model of the important concept of response detection limit, a concept that is usually neglected in the analysis of dose-response data with continuous responses. The methodology is illustrated with data from a study searching for compounds that neutralize the infection by a human immunodeficiency virus of brain glioblastoma cells.

KW - Biomolecular screening

KW - Constrained least squares

KW - Dose-response effects

KW - Effect size

KW - Hill equation

KW - Logit transformation

KW - Median-effect equation

KW - Michaelis-Menten equation

KW - Nonlinear regression

KW - Scaled beta distribution

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UR - http://www.scopus.com/inward/citedby.url?scp=84930807030&partnerID=8YFLogxK

U2 - 10.1080/10543406.2014.920871

DO - 10.1080/10543406.2014.920871

M3 - Article

C2 - 24905187

AN - SCOPUS:84930807030

SN - 1054-3406

VL - 25

SP - 757

EP - 780

JO - Journal of Biopharmaceutical Statistics

JF - Journal of Biopharmaceutical Statistics

IS - 4

ER -