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Pharma Tech Outlook | Friday, June 18, 2021
The COVID-19 pandemic has showcased a crucial need to develop effective drugs rapidly. However, setting a new drug is easier said than done. Drug discovery begins with a hypothesis that a target molecule or pathway inhibition or activation results in a therapeutic effect.
FREMONT, CA: There has been significant interest in comprehending ways to improve drug discovery success rate, and one such means is automation. Automation includes innovations that can transform drug development. Throughout the value chain of drug discovery, automation can increase laboratory efficiency, decrease overall attrition, and reduce costs. In addition, new technologies such as microfluidics, robotics, and artificial intelligence, combined with automated data analysis, can accelerate drug development and approval processes, helping patients access therapies faster.
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High-throughput screening (HTS) encompasses employing automated equipment to evaluate compound activity against specific biological targets quickly. The main advantage of using high-throughput screening is the ability to speedily and reproducibly test thousands to hundreds of thousands of agents (small molecules or functional genomics tools). Thus, HTS can be viewed as a rapid biological process scan that can quickly exclude candidates with inadequate or no effect from the drug discovery pipeline.
Automation allows for testing more hypotheses
In addition to reduced costs and reduced timelines, automation improves data accuracy, accuracy, reproducibility and traceability, allowing researchers to exploit high-quality data in hypothesis-driven research. Because of the automation accuracy, all plates in the assay will run under very similar conditions and ensure assay uniformity throughout the screen using plate-based controls. Furthermore, automation allows researchers to test more hypotheses and enable complex workflows and screening scenarios that can be difficult or impossible to achieve manually.
Robotics – Enhance the accuracy
Robotics improve overall process efficiency by creating efficient means of pre-set tasks. In addition, robots are unrelenting systems capable of parallel processing; they can manage multiple sequential steps simultaneously in any workflow without stopping or 'taking breaks.' As a result, robotic systems can significantly increase the accuracy and reproducibility of the process and the quality of data capture, which is difficult for researchers to achieve.
Artificial intelligence (AI) in drug discovery
Modern biology is increasingly rich in data, such as the vast amount of genetic data generated by thousands of genomic databases. However, these large datasets require appropriate analytical methods to yield statistically valid models that can make predictions. Artificial intelligence (AI) is used to capture and use these large datasets for early target identification and validation. AI refers to a machine's ability (such as a computer) to perform tasks in response to various environments. For example, machine learning (ML), an artificial intelligence subset, uses algorithms to learn and improve without reprogramming. Many stages of drug discovery can use ML.
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