Compound Ranking Based on a New Mathematical Measure of Effectiveness Using Time Course Data from Cell-Based Assays

Abstract

The half maximal inhibitory concentration (IC50) has several limitations that make it unsuitable for examining a large number of compounds in cytotoxicity studies, particularly when multiple exposure periods are tested. This article proposes a new approach to measure drug effectiveness, which allows ranking compounds according to their toxic effects on live cells. This effectiveness measure, which combines all exposure times tested, compares the growth rates of a particular cell line in the presence of the compound with its growth rate in the presence of DMSO alone. Our approach allows measuring a wider spectrum of toxicity than the IC50 approach, and allows automatic analyses of a large number of compounds. It can be easily implemented in linear regression software, provides a comparable measure of effectiveness for each investigated compound (both toxic and non-toxic), and allows statistically testing the null hypothesis that a compound is non-toxic versus the alternative that it is toxic. Importantly, our approach allows defining an automated decision rule for deciding whether a compound is significantly toxic. As an illustration, we describe the results of a cell-based study of the cytotoxicity of 24 analogs of novobiocin, a C-terminal inhibitor of heat shock protein 90 (Hsp90); the compounds were ranked in order of cytotoxicity to a panel of 18 cancer cell lines and 1 normal cell line. Our approach may also be a good alternative to computing the half maximal effective concentration (EC50) in studies searching for compounds that promote cell growth.

Measuring and Statistically Testing the Size of the Effect of a Chemical Compound on a ContinuousIn-VitroPharmacological Response Through a New Statistical Model of Response Detection Limit

“…In this case, it is not unusual that the observed dose-response curve of a compound j does not reach a clear plateau, even if the compound has a dose-response effect; as a result, no reliable estimator for the D (50) of compound j can be obtained by fitting equation (1) to the pairs ( D 1 , R 1 j ), …, ( D n , R nj ), and there are even cases in which no D (50) measure can be obtained (Diaz et al 2013). …”

“…Second, the D (50) measure, and indeed equation (1) alone, cannot be used to statistically test the null hypothesis that the investigated doses of the compound do not have a differential effect on the bioassay response, nor the null hypothesis that none of the investigated doses of the compound have an effect on the response (Diaz et al 2013). The reason is that the parameter space of equation (1), namely {( w, D (50) ) ∈ ℝ 2 ; D (50) > 0}, does not include the case in which R = constant (or R ≈ constant) for all D .…”

“…Admitting that the D (50) measure is widely used in both pharmacological and biological research, this measure has a number of limitations that hinder its applications to compound ranking and statistical testing in biomolecular screening (Diaz et al 2013). The limitations essentially stem from the fact that the D (50) has to be computed under the assumption of the existence of a dose-response effect and, therefore, the D (50) cannot be used to statistically test the null hypothesis of no dose-response effect for the compound, or the more general null hypothesis that no investigated compound dose has an important effect on the response.…”

“…The limitations essentially stem from the fact that the D (50) has to be computed under the assumption of the existence of a dose-response effect and, therefore, the D (50) cannot be used to statistically test the null hypothesis of no dose-response effect for the compound, or the more general null hypothesis that no investigated compound dose has an important effect on the response. Moreover, the D (50) measure cannot be reliably computed in many cases (Diaz et al 2013). An elaboration of this and other limitations is provided in Section 2.…”

Research Spotlight: The University of Kansas High-Throughput Screening Laboratory. Part I: Meeting Drug-Discovery Needs in the Heartland of America with Entrepreneurial Flair

“…All 19 cell lines, two normal and 17 cancerous, were tested simultaneously at multiple concentrations, for four different time points of compound exposure, for 24 small-molecule compound analogs (Table 1). The results of these studies have helped determine the IC 50 values for of each compound in multiple cell lines in parallel, to provide insight into structure–activity relationships to guide compound prioritization toward the goal of therapeutic development of novel, potent, cancer-specific agents for clinical use [2]. The value of this consolidation was the minimization of variation due to differences in handling compounds, chemical stocks, labs, assays methodology, cell lines and data processing, and to prioritize compounds for further study.…”