How Desirable Are Your IC50s?: A Way to Enhance Screening-Based Decision Making

Paolini, Gaia V.; Lyons, Richard A.; Laflin, Philip

doi:10.1177/1087057110384402

Cited by 12 publications

(12 citation statements)

References 9 publications

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“…Desirability provides a simple yet powerful approach to multi-criteria optimization. It is finding increasing utility in a number of applications in drug discovery including compound selection 16 , library design 17, 18 , molecular target prioritisation, central nervous system penetration 19 and estimating the reliability of screening data 20 .The concept was originally introduced by Harrington 15 in the area of process engineering and further refined by Derringer 21 . Desirability takes multiple numeric or categoric parameters measured on different scales and describes each by an individual desirability function.…”

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confidence: 99%

Quantifying the chemical beauty of drugs

Bickerton¹,

Paolini²,

Besnard³

et al. 2012

Nature Chem

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Druglikeness is a key consideration when selecting compounds during the early stages of drug discovery. However, evaluation of druglikeness in absolute terms does not adequately reflect the whole spectrum of compound quality. More worryingly, widely used rules may inadvertently foster undesirable molecular property inflation as they permit the encroachment of rule-compliant compounds toward their boundaries. We propose a measure of druglikeness based on the concept of desirability called Quantitative Estimate of Druglikeness (QED). The empirical rationale of QED reflects the underlying distribution of molecular properties. QED is intuitive, transparent, straightforward to implement in many practical settings and allows compounds to be ranked by their relative merit. We extend the utility of QED by applying it to the problem of molecular target druggability assessment by prioritizing a large set of published bioactive compounds. The measure may also capture the abstract notion of aesthetics in medicinal chemistry.The concept of druglikeness provides useful guidelines for early stage drug discovery 1, 2 . Analysis of the observed distribution of some key physicochemical properties of approved drugs, including molecular weight, hydrophobicity and polarity, reveals they preferentially occupy a relatively narrow range of possible values 3 . Compounds that fall within this range are described as "druglike." Note that this definition holds in the absence of any obvious structural similarity to an approved drug. It has been shown that preferential selection of druglike compounds increases the likelihood of surviving the well-documented high rates of attrition in drug discovery 4 .Druglikeness can be rationalized by consideration of how simple physicochemical properties impact molecular behavior in vivo, with particular respect to solubility, permeability, metabolic stability and transporter effects. Indeed druglikeness is often used as a proxy for Correspondence should be addressed to A.L.H. (a.hopkins@dundee.ac.uk).. Additional InformationSupplementary information is available online at XXXX. We have implemented QED as simple functions in Python, SQL (Structure Query Language), Accelrys Pipeline Pilot and Microsoft Excel, the codes for which are available in the Supplementary Information. The Microsoft Excel example also includes data on the 771 oral drugs used to derive the desirability functions. Pre-calculated QED values and desirability functions for 657,736 compounds from ChEMBL (release ChEMBL09) are also available. Author contributions Europe PMC Funders Author ManuscriptsEurope PMC Funders Author Manuscripts oral bioavailability. However, druglikeness provides a broad composite descriptor that implicitly captures several criteria, with bioavailability amongst the most prominent.In practical terms, assessment of druglikeness is most commonly manifested as rules, the original and most well known of which is Lipinski's Rule of Five (Ro5) 5 . The rule states that a compound is more likely to exhibit poor a...

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confidence: 99%

Quantifying the chemical beauty of drugs

Bickerton¹,

Paolini²,

Besnard³

et al. 2012

Nature Chem

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1,609

1,269

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“…Additionally, the appropriateness of IC 50 values as a summary variable of dose-response curves for genetic analysis has been contested. 29 , 32 , 49 – 51 IC 50 values, corresponding to the potency of a compound, are physiologically relevant. Nonetheless, biological and statistical assumptions are not always met, namely that differential response can be defined by one parameter from a complex nonlinear model.…”

Section: Discussionmentioning

confidence: 99%

Immune cell-based screening assay for response to anticancer agents: applications in pharmacogenomics

Frick¹,

Fedoriw²,

Richards³

et al. 2015

PGPM

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BackgroundInterpatient variability in immune and chemotherapeutic cytotoxic responses is likely due to complex genetic differences and is difficult to ascertain in humans. Through the use of a panel of genetically diverse mouse inbred strains, we developed a drug screening platform aimed at examining interstrain differences in viability on normal, noncancerous immune cells following chemotherapeutic cytotoxic insult. Drug effects were investigated by comparing selective chemotherapeutic agents, such as BEZ-235 and selumetinib, against conventional cytotoxic agents targeting multiple pathways, including doxorubicin and idarubicin.MethodsSplenocytes were isolated from 36 isogenic strains of mice using standard procedures. Of note, the splenocytes were not stimulated to avoid attributing responses to pathways involved with cellular stimulation rather than toxicity. Cells were incubated with compounds on a nine-point logarithmic dosing scale ranging from 15 nM to 100 μM (37°C, 5% CO2). At 4 hours posttreatment, cells were labeled with antibodies and physiological indicator dyes and fixed with 4% paraformaldehyde. Cellular phenotypes (eg, viability) were collected and analyzed using flow cytometry. Dose-response curves with response normalized to the zero dose as a function of log concentration were generated using GraphPad Prism 6.ResultsPhenotypes were quantified using flow cytometry, yielding interstrain variation for measured endpoints in different immune cells. The flow cytometry assays produced over 16,000 data points that were used to generate dose-response curves. The more targeted agents, BEZ-235 and selumetinib, were less toxic to immune cells than the anthracycline agents. The calculated heritability for the viability of immune cells was higher with anthracyclines than the novel agents, making them better suited for downstream genetic analysis.ConclusionUsing this approach, we identify cell lines of variable sensitivity to chemotherapeutic agents and aim to identify robust, replicable endpoints of cellular response to drugs that provide the starting point for identifying candidate genes and cellular toxicity pathways for future validation in human studies.

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“…The curve is usually summarized by a single parameter such as the curve’s inflection point (e.g. the EC/IC 50 ) [10] or the slope of the curve (called the hill-slope ) [11]. Perhaps the most widely used summary in pharmacogenomics cell line experiments is the IC 50 , which represents the concentration where the response achieves 50% of maximal activity [3-9].…”

Section: Discussionmentioning

confidence: 99%

“…This notion of IC 50 can be generalized further such that IC X is the concentration at which the response is X% between minimal and maximal activity. IC 50 s (and their IC X cousins) have been widely used in areas such as toxicology, pharmacology/pharmacogenomics, and industrial drug development [10]. Its popularity derives from the fact that it is a concise and interpretable summary of a drug’s activity, which conveys an indication of the drug’s potency .…”

Section: Discussionmentioning

confidence: 99%

Beyond IC50s: Towards Robust Statistical Methods for in vitro Association Studies

Motsinger‐Reif¹

2014

J Pharmacogenomics Pharmacoproteomics

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Cell line cytotoxicity assays have become increasingly popular approaches for genetic and genomic studies of differential cytotoxic response. There are an increasing number of success stories, but relatively little evaluation of the statistical approaches used in such studies. In the vast majority of these studies, concentration response is summarized using curve-fitting approaches, and then summary measure(s) are used as the phenotype in subsequent genetic association studies. The curve is usually summarized by a single parameter such as the curve’s inflection point (e.g. the EC/IC50). Such modeling makes major assumptions and has statistical limitations that should be considered. In the current review, we discuss the limitations of the EC/IC50 as a phenotype in association studies, and highlight some potential limitations with a simulation experiment. Finally, we discuss some alternative analysis approaches that have been shown to be more robust.

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How Desirable Are Your IC50s?: A Way to Enhance Screening-Based Decision Making

Cited by 12 publications

References 9 publications

Quantifying the chemical beauty of drugs

Quantifying the chemical beauty of drugs

Immune cell-based screening assay for response to anticancer agents: applications in pharmacogenomics

Beyond IC50s: Towards Robust Statistical Methods for in vitro Association Studies

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