Meso Scale Discovery (MSD) has a proprietary platform for measuring analytes in biological samples. MSD analysis has several assay options, including MSD biomarker assay, ADA assay development, and Meso Scale ELISA assays. MSD assay helps researchers profile and characterize analytes such as intercellular signaling proteins and cytokines, directly impacting drug discovery and development studies. This technology combines electrochemiluminescence detection and multi-array technology to deliver multiplex capacities and enhanced sensitivity. However, data is the new currency. Hence leveraging the power of MSD analysis through data-driven drug development is crucial for MSD assay development and subsequent analysis.
Data-Driven Drug Development
Drug discovery and development process is a complex initiative. Developing a new drug requires around 2 to 3 billion dollars and may take up to 10 to 12 years to reach the market. Besides, many drug products fail later in clinical trials, increasing the number of losses exponentially. Hence data-driven drug development can help identify potential compounds early during drug discovery and development.
During the initial wave of big data in the pharmaceutical industry, researchers were able to unearth previously unexplored biological data. Today researchers have an abundance of data to leverage. Scientists can now analyze large chunks of data from clinical trials, research papers, medical records, and patents. Leveraging this data to anticipate side effects, determine new connections, and enhance drug molecules revolutionized medical and pharmaceutical sciences. Data-driven drug development includes leveraging big data to create and identify therapies with enhanced potential. However, this requirement necessities the need for artificial intelligence.
The influence of such a large amount of data in a short period has resulted in the development of innovative technology such as artificial intelligence. Such technology can help monitor and understand insights from medical records, dissertations, trial data, patient data, and congresses. With increased biological data, researchers will require advanced tools to analyze them. Today pharmaceutical companies are increasingly focusing on acquiring computing power, machine learning, and artificial intelligence to manage and analyze data. Companies have not only embraced data-driven approaches but have tools to manage big data using technology such as network analysis, machine learning, normalization, and computer vision.
Also Read: Challenges and Solutions for Good Practices Laboratory-Compliant GLP Labs in the Era of Biologics
Robotics has slowly entered the field of medical and pharmaceutical sciences. Although the use of robotics to physically test and experiment in a lab is still not welcomed by many, it is used widely for searching public databases. With advanced robotic techniques and artificial intelligence, researchers can search large amounts of data and generate relationships between biological elements such as proteins, genes, drugs, and diseases. This application becomes even more crucial as understanding and establishing relationships manually is the most challenging job, especially when 1000s of biomolecules are involved. Besides, identifying new connections between biological elements can open new avenues for therapeutic interventions and therapy, thereby accelerating drug discovery and development. Moreover, it can help identify new targets for drug products and enable drug repurposing.
Also Read: MSD Assay Services: A Comprehensive Guide to Understanding and Optimizing Your Results
In conclusion, data-driven drug development will not only accelerate drug development timelines but also and facilitate sharing of biological data. More data means researchers can acquire meaningful insights and train machine learning systems better.
