Efficient optimization of ELISAs

Reiken, Steven; Wie, Bernard J. Van; Sutisna, Himawan; Kurdikar, Devdatt L.; Davis, William C.

doi:10.1016/0022-1759(94)90157-0

Cited by 13 publications

(4 citation statements)

References 3 publications

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“…The ELISA system is the most common method to determine the quantitative changes of cytokines/ chemokines [4] . Due to the limitation of the classic method, only one parameter per run can be analysed.…”

Section: Introductionmentioning

confidence: 99%

Validation of novel multiplex technologies

Akyüz¹

2017

Adv Precis Med

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Cytokine and chemokine levels in body fluid provide information of altered conditions in patients. The parallel analysis of multiple factors, such as cytokines, from small sample sizes is an interesting approach for the assessment of in vivo activation signatures and functionality after ex vivo stimulation. One interesting application is for therapy monitoringsuch as safety data, pharmacodynamics, evidences for mode-of-action and side effects-which is particularly useful for accompanying early phase clinical trials. There are different platforms for multiplex analyses of ligands available. An assay validation of three different platforms for simultaneous quantification of cytokines (Luminex Bio-Plex ® 200, Meso Scale Discovery ® and Ella ® ) was performed in this study. The comparison of multiplex platforms, which use different ways of achieving parallel measurements of biomarkers, reveal the performance strengths and weaknesses. We showed examples of in-house assessments of intra-and inter-assay variations, determination of the range and recovery of classical immunological serum markers and discussed advantages and disadvantages of these three platforms in relation to the question addressed. All the platforms show low intra-assay variances. The Luminex platform shows a high inter-assay variance for the majority of parameters, whereas the MSD and Ella platforms show low inter-assay variances.

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Section: Introductionmentioning

confidence: 99%

Validation of novel multiplex technologies

Akyüz¹

2017

Adv Precis Med

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“…To our knowledge, this is the first time that a fractional factorial design has been used to assess the effect of several assay conditions on outcomes [ 16 ]. Fractional factorial design reduces the total number of experiments while determining the parameters that give the maximum sensitivity range with the maximum accuracy, providing valid results for multifactorial effects [ 42 ].…”

Section: Discussionmentioning

confidence: 99%

Analysis of factors affecting the variability of a quantitative suspension bead array assay measuring IgG to multiple Plasmodium antigens

et al. 2018

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Reducing variability of quantitative suspension array assays is key for multi-center and large sero-epidemiological studies. To maximize precision and robustness of an in-house IgG multiplex assay, we analyzed the effect of several conditions on variability to find the best combination. The following assay conditions were studied through a fractional factorial design: antigen-bead coupling (stock vs. several), sample predilution (stock vs. daily), temperature of incubation of sample with antigen-bead (22°C vs. 37°C), plate washing (manual vs. automatic) and operator expertise (expert vs. apprentice). IgG levels against seven P. falciparum antigens with heterogeneous immunogenicities were measured in test samples, in a positive control and in blanks. We assessed the variability and MFI quantification range associated to each combination of conditions, and their interactions, and evaluated the minimum number of samples and blank replicates to achieve good replicability. Results showed that antigen immunogenicity and sample seroreactivity defined the optimal dilution to assess the effect of assay conditions on variability. We found that a unique antigen-bead coupling, samples prediluted daily, incubation at 22°C, and automatic washing, had lower variability. However, variability increased when performing several couplings and incubating at 22°C vs. 37°C. In addition, no effect of temperature was seen with a unique coupling. The expertise of the operator had no effect on assay variability but reduced the MFI quantification range. Finally, differences between sample replicates were minimal, and two blanks were sufficient to capture assay variability, as suggested by the constant Intraclass Correlation Coefficient of three and two blanks. To conclude, a single coupling was the variable that most consistently reduced assay variability, being clearly advisable. In addition, we suggest having more sample dilutions instead of replicates to increase the likelihood of sample MFIs falling in the linear part of the antigen-specific curve, thus increasing precision.

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“…For example, six factorial designs were used to study the effects of medium composition, incubation conditions, and associated microflora on the production of type G Clostridium botulinum toxin (Called et al 1992) in vitro. A fractional factorial design was used to optimize enzymelinked immunosorbent assay tests (Reiken et al 1994), and a 2 4 factorial was used to optimize the conditions for freezing rat liver slices (Maas et al 2000). Similar methods have been used to optimize the signal in DNA microarray experiments (Wildsmith et al 2001).…”

Section: Introductionmentioning

confidence: 99%

Use of Factorial Designs to Optimize Animal Experiments and Reduce Animal Use

Shaw¹,

Festing²,

Peers³

et al. 2002

ILAR Journal

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Optimization of experiments, such as those used in drug discovery, can lead to useful savings of scientific resources. Factors such as sex, strain, and age of the animals and protocol-specific factors such as timing and methods of administering treatments can have an important influence on the response of animals to experimental treatments. Factorial experimental designs can be used to explore which factors and what levels of these factors will maximize the difference between a vehicle control and a known positive control treatment. This information can then be used to design more efficient experiments, either by reducing the numbers of animals used or by increasing the sensitivity so that smaller biological effects can be detected. A factorial experimental design approach is more effective and efficient than the older approach of varying one factor at a time. Two examples of real factorial experiments reveal how using this approach can potentially lead to a reduction in animal use and savings in financial and scientific resources without loss of scientific validity.

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Efficient optimization of ELISAs

Cited by 13 publications

References 3 publications

Validation of novel multiplex technologies

Validation of novel multiplex technologies

Analysis of factors affecting the variability of a quantitative suspension bead array assay measuring IgG to multiple Plasmodium antigens

Use of Factorial Designs to Optimize Animal Experiments and Reduce Animal Use

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