The pharmaceutical industry & Automated Patch Clamp
The pharmaceutical industry is always in search of new drugs to improve patient outcomes. It is a highly regulated industry, and companies must comply with strict safety, efficacy, and quality standards. Regulatory compliance is essential to ensure that drugs are safe and effective, and to maintain public trust.
Automated patch clamping provides the pharmaceutical industry with a powerful tool for studying ion channels in cells and their response to drugs. Automating the process ensures significant improvements in laboratory efficiency, data quality, and throughput, which can ultimately accelerate the drug discovery process and improve patient outcomes.
Increased laboratory efficiency
By using automated patch clamp systems, you are automating many of the time-consuming steps involved in manual patch clamp recordings. Thereby, saving significant time that staff can use for data analysis and assay optimization. Automating the process enables the pharmaceutical industry to test more compounds in a shorter amount of time, leading to increased efficiency in drug discovery.
High Throughput Screening (HTS)
Automated patch clamp enables the pharmaceutical industry to test large numbers of compounds in a short amount of time, which can help to identify potential drug candidates more quickly. This can speed up the drug development process and help to get new drug discoveries and treatments to market faster.
Improved data quality & accuracy
Automated patch clamp techniques provide highly accurate and reliable data, which helps the pharmaceutical industry make better-informed decisions about drug development. Automating the process also eliminates the human error factor, which is associated with manual patch clamp recordings. Thereby, improving the accuracy and reliability of data.
Reduced data variability
With automated patch clamp, researchers can reduce the variability of data between different experiments, which improves the consistency and reliability of their results. This helps ensure that the data generated by different labs are more comparable, leading to better data sharing and more efficient drug discovery.