Efficient Drug Discovery Using Big Data Analytics


Drug discovery is a thorough process that involves study of empirical data for identifying patterns, testing theories, and understanding efficacy of treatments. However, sudden outbreak of pandemics such as COVID-19 calls for efficient drug discovery at a quick pace. While pharmaceutical industry struggles to find an effective medicine against new diseases, big data analytics can help in overcoming the challenges faced by drug discovery processes.

Following are some ways in which big data analytics facilitate efficient drug discovery:

  1. Accelerate drug discovery & development: The ability to efficiently search vast data sets of patents, scientific journals, clinical trials, etc. helps the researchers to examine previous results. Predictive analysis of the data provides an insight into information necessary for generating desired results.
  2. Optimize & improve efficacy of clinical trials: Clinical trials require resources such as time and finances. Also, they require right set of patients. Big data analytics helps in identifying patients suitable for the trials, remote patient monitoring, reviewing previous trial results and identifying potential side effects of drug being tested before it emerges in the population.
  3. Target specific patient populations more effectively: Patients may respond to a treatment differently, even if they have same disease conditions. The thorough analysis of data available from genomic sequencing, medical sensors, etc. enables pharmaceutical industry to spot trends and patterns for developing more targeted medications.
  4. Better insight into patient behavior to improve drug delivery: Collection and analysis of data helps in keeping a track of patient behavior. An example is that a drug manufacturer analyzed through sensor data that patients are forgetting to take their medicine. The team came up with a cloud-based solution that would push reminder messages directly to patient’s mobile to help them adhere to medical routine for better results.
  5. Improve safety & risk management: Data from various sources can provide information about impending product safety issues and offer pharmaceutical companies enough time to manage the risks by developing a solution.
  6. Gain improved insight into marketing & sales performance: As the competition in generic drug market has increased, drug manufacturers can increase their sales effectiveness by employing big data analytics. Data analysis assists in recognizing niche and under reserved markets, understanding efficiency of sales strategies and getting real-time customer feedback.
  7. Drug candidate selection & pipeline development: Data analysis of existing drug databases, protein structure datasets and chemical properties data eases the process of drug candidate selection by offering detailed study of the binding properties. This reduces the timeframe of drug discovery process.
  8. Orphan drugs, rare diseases and drug repurposing: Orphan drugs are the drugs that are used for diagnosis, treatment or prevention of life threatening diseases or disorders. The development of these drugs faces several challenges such as limited population for study, fewer individual reports for study of adverse reactions, etc. Big data analytics help in resolving these challenges by gathering information from wide number of sources.

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