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Automating Dose Response Curves: How Liquid Handling Boosts Accuracy & Efficiency

Written by Nila Lê | Sep 10, 2024 2:30:00 PM

Dose response experiments allow researchers to examine the effect of increasing treatment dosages on a biological system1. These experiments are used to produce graphs called dose response curves, which let scientists examine multiple parameters of a target compound, such as potency, toxicity, and mechanism of action (Fig. 1). IC50 and EC50 values are essential for screening and optimizing drug candidates2,3. Both are determined using accurate dose response curves.

Deriving reliable data from dose response curves comes with several pitfalls and challenges. A central roadblock is accurate liquid dispensing, which is paramount for ensuring the correct dosage is added to the right well. In today’s market, researchers must match faster turnaround times with accurate liquid dispensing to remain competitive and identify promising compounds quickly. This article will explore the challenges of generating dose response curves with manual methods and highlight how automation in liquid handling solves these problems and gives researchers a competitive edge.

Figure 1. Dose response curves give researchers crucial information about different treatments but must be performed accurately for meaningful conclusions to be derived. (Source)

Challenges of Manual Liquid Handling in Dose Response Curves

Manual liquid handling has been the mainstay of biomedical research because, until recently, there simply hasn’t been a viable alternative. This lack of options means researchers have constantly battled several challenges that have consistently hampered sensitive workflows like dose response experiments.

Pipetting Errors

While modern pipettes have improved accuracy, even experienced researchers will find significant variation between replicates they’ve pipetted by hand. The reason is simple: manual pipetting is inherently error-prone4.

Inconsistent Workflows

Reproducibility within and between laboratories is crucial as biomedical research becomes an increasingly collaborative and global enterprise. Operators often have different manual liquid handling methods5, increasing workflow variation and hindering reproducibility and drug discovery efforts.

Time-consuming

When budding scientists dream of curing diseases, they rarely envision hours of tedious manual liquid dispensing. This is an unfortunate reality, however. Time spent pipetting multiwell plates can quickly consume the entire workday for highly-trained scientists. The opportunity cost here is massive, as researchers are diverted from tasks like experimental design and data interpretation6.

Limit Throughput

Drug discovery is a highly competitive market, and manual liquid dispensing limits the amount of data researchers can produce. There simply isn’t enough time in the day for manual methods to meet the throughput requirements of modern biomedical research.

While manual handling methods have been the default that has brought us to where we are today, savvy researchers should abandon these outdated methods and adopt automated liquid handling for generating dose response curves.

Benefits of Liquid Handling Automation for Dose Response Curves

Liquid handling automation thrives in areas where manual methods flounder, bringing unique benefits for researchers performing all-important dose response curves. The I.DOT Liquid Handler from DISPENDIX delivers these benefits in a one-stop solution (Fig. 2).

Figure 2. The I.DOT Liquid Handler increases the accuracy, throughput, and reproducibility of dose response curves while helping researchers stick to their budgets.

  • Enhanced accuracy. Liquid handling automation provides more accurate dispensing than manual methods. This means more accurate dose response curves that facilitate better data-driven decision-making.
  • Improved reproducibility. Automation means experiments can be performed more consistently within and between institutions. This produces more reliable dose response data and facilitates collaboration for streamlined drug discovery.
  • Increased efficiency. Automation means dose response experiments can be performed faster and on a smaller scale, improving time and resource efficiency6.
  • Boosted throughput. Automation removes work-day time barriers and is faster than manual pipetting. This means researchers can do more dose response experiments when adopting automated liquid handling techniques7.
  • Reduced costs. Liquid handling automation reduces resource and labor costs, offering a budget-friendly way to generate large volumes of accurate dose response data6.

How Liquid Handling Automation Works

  • Serial dilutions. Automated liquid handling facilitates the sequential dilution of treatment concentrations before adding them to the plate to ensure the desired dosages are used8. The repetitive nature of this task means that automation avoids errors introduced by manual methods, which can yield inaccurate and misleading results.
  • Workflow compatibility. Automated liquid handlers like the I.DOT Liquid Handler from DISPENDIX are versatile, meaning they can fit seamlessly into pre-existing workflows. This means minimal disruption to research output during the switch to automation.
  • Precision requirements. Modern drug discovery demands precision that few instruments can meet. The I.DOT Non-Contact Dispenser provides non-contact dispensing with integrated DropDetection technology to ensure accurate, linear dose response curves. The I.DOT's non-contact dispensing capabilities reduce cross-contamination, further enhancing overall precision.
  • Budget. The I.DOT Non-Contact Dispenser allows precise droplet dispensing of volumes as low as 0.1 nL. Combined with a dead volume of 1 μL, this means the I.DOT Liquid Handler uses fewer reagents, which quickly offsets the initial cost of investment.

Conclusion

Automating liquid handling for dose response curves significantly enhances the accuracy, efficiency, and reproducibility of dose response experiments. By minimizing human error and increasing throughput, automated systems like the I.DOT Liquid Handler empowers researchers to generate more reliable data while saving time and reducing costs. Automation accelerates drug development and ensures that valuable resources are utilized effectively, giving researchers the green light to solve pressing biomedical problems.

Optimize your dose response studies now! Unlock precise and reproducible results with DISPENDIX. Discover how our advanced liquid handling technology can elevate your dose response experiments. Download the I.DOT brochure today to learn more!

References

  1. Calabrese EJ. The Emergence of the Dose-Response Concept in Biology and Medicine. Int J Mol Sci. 2016;17(12):2034. doi:10.3390/ijms17122034
  2. He Y, Zhu Q, Chen M, et al. The changing 50% inhibitory concentration (IC50) of cisplatin: a pilot study on the artifacts of the MTT assay and the precise measurement of density-dependent chemoresistance in ovarian cancer. Oncotarget. 2016;7(43):70803-70821. doi:10.18632/oncotarget.12223
  3. Singh A, Raju R, Mrad M, Reddell P, Münch G. The reciprocal EC50 value as a convenient measure of the potency of a compound in bioactivity-guided purification of natural products. Fitoterapia. 2020;143:104598. doi:10.1016/j.fitote.2020.104598
  4. Guan XL, Chang DPS, Mok ZX, Lee B. Assessing variations in manual pipetting: An under-investigated requirement of good laboratory practice. J Mass Spectrom Adv Clin Lab. 2023;30:25-29. doi:10.1016/j.jmsacl.2023.09.001
  5. Lippi G, Lima-Oliveira G, Brocco G, Bassi A, Salvagno GL. Estimating the intra- and inter-individual imprecision of manual pipetting. Clinical Chemistry and Laboratory Medicine (CCLM). 2017;55(7). doi:10.1515/cclm-2016-0810
  6. Holland I, Davies JA. Automation in the Life Science Research Laboratory. Front Bioeng Biotechnol. 2020;8(571777). doi:10.3389/fbioe.2020.571777
  7. Schneider G. Automating drug discovery. Nat Rev Drug Discov. 2018;17(2):97-113. doi:10.1038/nrd.2017.232
  8. Donev AN, Tobias RD. Optimal Serial Dilutions Designs for Drug Discovery Experiments. Journal of Biopharmaceutical Statistics. 2011;21(3):484-497. doi:10.1080/10543406.2010.481801