Drug development focuses on finding cures for known disease-related targets, or making diseases, conditions and viruses more manageable. Drug R&D processes are driven by significant improvements in computing power and advances in robotics and biological technology.
But in most cases, the drug discovery process can be costly and time-consuming. Based on various statistics, bringing a new drug to market could cost at least $ 4 billion and take 10 to 15 years. And more importantly, less than 10% actually reaches the market.
Also, before a drug is available, it must be tested in clinical trials to determine its effectiveness, safety, and the correct dosage.
Drug R&D is built on accurate data, research, patient and peer-reviewed trials, to ensure continued improvements, since data is key to identifying areas that need more work to get the desired results.
Data-driven drug development resources and methodologies contribute to the ever-growing volume of data from large-scale biological experiments, and offer significant benefits to biotech companies. Let’s take a closer look at the hottest three.
Data Science. Interpreting big data in the drug development community shortens project lead times and decreases patient dropout through better early decision making.
Artificial intelligence (AI) is helping to accelerate the discovery and development of new drugs, as well as improve predictions of drug affinity.
Deep learning is a technology that efficiently processes all the information needed for scientific research. Using it, scientists can scan and analyze publications and databases on various drugs.
These are examples of long-term data-driven trends. But first, another challenge needs to be solved. Many biotech and pharmaceutical companies are wary of investing heavily in improving the analytical capabilities of big data, in part because there are only a few examples of colleagues creating more value out of it. But investments will grow. The road to artificial intelligence, deep learning and PM is challenging. Still, companies need to understand that the possibilities of big data in industry R&D are real, and companies that succeed will be well rewarded. But for this, the entire organization should first go through the significant digital transformation processes.
Drug discovery and research is a time and resource-intensive process, and pharmaceutical organizations need every advantage to maximize their return on investment and reduce development time. For a more convenient and efficient work process, companies need to use specialized analytics and reporting software that can help to gain great results and provide all the necessary data. Here are a few basic ones.
Ensures reproducibility of results
It provides excellent opportunities in drug discovery through multifaceted utilization of data collection, pre-processing, analysis, and inference.
Saves image analysis time
Drug discovery software that includes image analysis features automates and leverages innovative technology that significantly cuts down on disease image analysis time, testing, and going to market.
Improves the quality, relevance, and impact of aggregated data
Drug discovery software uses technology for the benefit of pharmaceutical companies, allowing them to create new drugs to solve severe problems for patients, create calculations that generate data, identify potential targets, and identify possible defects of developing drugs to help speed up the drug discovery process.
Helps pharmaceutical companies compete in the drug market
Drug software helps researchers, chemists and scientists scale-up efforts by standardizing processes, storing and duplicating data, and dramatically reducing the time and resources spent on developing new drugs and getting to the drug market.
Software-based drug discovery and development methods play an essential role in the development of biologically active compounds. It allows to develop new pharmaceutical drugs and test whether a newly created drug will be effective in treating a particular disease.
Clinical trial management
This type of software is used by researchers to define, implement, and track clinical trial results. Clinical trial administrators also use this type to find and schedule participants and track their participation in research.
Quality Management Software (QMS) helps businesses evaluate and maintain the quality of drugs and customer service. QMS is used to define and implement quality specifications based on client requirements following industry standards and regulations.
CRM in Pharma and Biotech
CRM for pharmaceutical and biotech companies include specialized capabilities that are part of the pharmaceutical sales model. CRM for Pharmaceuticals is optimized to educate clinicians and other stakeholders about products to improve patient outcomes.
Disease management software is designed to connect to the EHR and other health data sources to manage, monitor, and process patient data. It’s also used by pharmacy companies for post-marketing monitoring.
AI-powered drug discovery companies are using computing platforms to mitigate risk through preclinical research and to promote candidates to the clinic through industry partnerships and investor partnerships.
For example, BERG uses a data-driven approach to drug discovery. Their software uses AI to process massive amounts of biological data to show you what happens to your health on the road to cancer.
Another company, Cloud Pharmaceuticals, is a leader in the computer-aided design of new drugs and their development based on information. The organization improves drug discovery and development, delivering tangible outcomes and real value to partners.
While there may still be some challenges in drug discovery and development, there are several positive factors for healthcare organization and medical specialists.
Data governance is a crucial aspect of the R&D process. Not only does this assist them in understanding the data they are getting, but also informing decisions around complex IT infrastructures and applications that support data compliance.
Besides, the regulatory environment and the high frequency of mergers and acquisitions (M&A) are among the conditions that distinguish digital transformation in pharmaceuticals.
Thus, protecting sensitive data in these industries is a matter of survival in terms of potential penalties for non-compliance with any industry and government regulations.
Use the analytics and visualization
The analysis and visualization of the chemical space encompassed by public, commercial, in-house and virtual collections of compounds have found many applications in diversity analysis, data mining, virtual screening, library design, and prioritization of screening campaigns. With the help of visualization, it is possible to apply a variety of methods and approaches to explore the chemical space of molecular databases and other substances.
Make the right Workflows
During clinical trial programming and data management, many business processes are used to ensure that different tasks and activities are performed sequentially. Thus, the goal of the workflow is to create an abstraction of all events that balances control with the flexibility to deviate from the specification as needed.
Jump into the cloud
Cloud computing provides an opportunity to quickly configure the system and easily scale compute, storage, and other infrastructure resources. Additional benefits include mobility and immediate access to centralized real-time data from any device, anywhere, reduced cost and greater R&N speed, improved efficiency, enhanced agility, superior storage and data analysis, and many others.
Sooner or later, the pharmaceutical industry will have to go through all the improvements and rapid development. And now is the best time for that. However, whenever a company decides to do this, transformation requires professionals who can work with data in this area, understand regulatory issues, provide quality results, and set up all processes.
Want to know more? Learn more about how we can help to build data-driven drug development and discovery platform.
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