Data Analytics Redefining Drug Discovery & Development

A guest article by: Prashant Sharma

As an industry, we know it takes 10-15 years and billions of dollars to bring a new drug to market. Adding to that challenge is the knowledge that less than 12% of drugs make it through clinical development. Data Analytics services provide solutions to accelerate drug discovery and improve patient outcomes. With advances in high-throughput technologies and data management systems, vast datasets can now be applied in biomedicine to provide insights needed to make more informed decisions.

Every attempt to discover something is a great bet. Its success depends on finding a target and identifying an effective compound for clinical trials. Thousands of possible paths collapse into one. Decisions are made about what routes are valid and which aren’t; these decisions are data-driven.

Quantitative and qualitative information will be examined for predictive modeling, where the hypotheses will be tested against each other. Ideally, the more data in a model, the better its predictions should get. However, the quantity of data available here causes another problem – figuring out which bits apply to the goal and which ones are repetitive or misleading enough to make the model come up with false conclusions. On top of it all, this system should ensure uniform structure and labeling of data so that they can be used effectively by them. Otherwise, it’s also essential that any machine-readable health record systems consistently structure and tag their information if they work towards standardization. That task becomes even more challenging when you’re dealing with hundreds of disconnected sources from where your collected data comes through, making drug development programs vulnerable to bottlenecks regarding standardization problems.

The Bioinformatics solutions for early R&D discovery in pharma include extraction, management, and analysis of various multi-OMICs data. These are developed and designed to prepare the data analysis for customized workflows, thus enabling informed decision-making through drug development. Our clients benefit from the synergy of our bioinformatics team, who analyze and interpret complex data; and our R&D IT engineers, who synthesize custom data management solutions.

The Data Analytics and Insights help you make evidence-based decisions and help you-

·?????? Produces novel drugs

·?????? Develop a healthy pipeline

·?????? Find additional uses for new and existing compounds

·?????? Identify the right subpopulations for your trials

·?????? Augment your team with proven expertise

·?????? Maximize the likelihood of clinical trial success

Structuring and analyzing the data

To speed up your journey of discovery, it's essential to make the path shorter, simpler, and more clearly marked. By combining scientific expertise with technical infrastructure, you can create a community of biologists, medicinal chemists, pharmacologists, and engineers who work at the intersection of science and technology to help structure your data.

The scientifically curated datasets meet the FAIR criteria for findability, accessibility, interoperability, and reusability. They are then extracted, transformed, and loaded, creating demonstrable value that all life sciences companies appreciate.

Finally, we make data the main focus of the drug discovery process. We build semantic models, automated pipelines, search tools, and visualizations that simplify the data analytics process and produce clear, precise results, empowering our customers’ decision-making and accelerating their discovery.

Therefore, how do we unlock the real future of data analytics in drug discovery? It is a matter of strategic approach:

1. FAIR Data Principles: These principles ensure that data can be found, accessed, interoperated, and reused, which makes it high-quality and usable across different research groups.
2. Data and AI: AI can analyze enormous datasets and spot patterns hidden from human sight. In this manner, potential side effects can be anticipated, the compound's effectiveness can be calculated, and new pharmacological targets can be found.
3. Combining Domain Knowledge and Tech Expertise: The best outcomes are obtained when combining scientific knowledge and data science abilities. This guarantees that researchers make relevant inquiries about the data they analyze and appropriately interpret their findings.

The integration of Artificial Intelligence (AI) and data analytics is further changing the pharmaceutical industry's landscape, particularly in drug discovery, development, and delivery. AI-enabled virtual screening techniques will expedite the analysis of vast chemical libraries, accelerating the identification of therapeutic candidates and lead compounds. AI’s capabilities extend to enabling precise medicine, categorizing patients, predicting therapy responses, and customizing medicines based on genomic, proteomic, and clinical data analysis.

Data analytics is no longer an indulgence but a means for attaining drug discovery breakthroughs. The data and analytics solutions empower innovation in life sciences from molecule to market. It helps you harmonize heterogeneous data sets, applying innovative bioinformatics know-how and technologies to accelerate drug discovery & development with reliable and result-oriented insights. By accepting challenges and adopting a strategic approach, researchers can use this information to become a potent weapon against diseases. The days when patients had to wait months for their health condition to improve are gone; now, future medicine is quicker, more efficient, and, most importantly, brings hope earlier than ever.