From Lab to Shelf with Powerful Data Analytics, Health News, ET HealthWorld

by Ram Mudaliar

Over the past two years, content streaming sites such as Netflix and Amazon have grown in popularity around the world. There is no doubt that the platforms have been able to generate great interest among subscribers who pay a premium to binge watch on these platforms. It’s amazing how Netflix has been able to suggest types of movies or TV series that will grab a subscriber’s attention and make sure they stay hooked. Not only that, Netflix also knows what kind of content to produce to meet the ever-changing interests of the audience. The answer lies in the data captured in the background and analyzed by Netflix analysts to provide the subscriber with the most appealing list of movie or TV series options.

Importance of data in clinical research
We all know the importance of clinical research, given the increased focus in recent years on bringing vaccines to market quickly, thereby saving millions of lives. Data analysis has played a key role in the discovery of new drugs and vaccines that have been identified to limit the side effects of disease or save people’s lives. The data produced by the thousands of volunteers participating in clinical trials has been documented, analyzed and provided as evidence to regulatory authorities to obtain approvals in a highly competitive market.

Capturing research data starts in the lab, through preclinical trials (animal studies), clinical trials (human research) and even in the real world (Pharmacovigilance). With research becoming more global than ever, data analytics has an important role to play in identifying risks early in the research cycle, resulting in course corrections, investing in the right strategy, saving time and resources. Data analysis also guides researchers in channeling their efforts towards the most appropriate methods that can yield the best results.

Contract research organizations (CROs) and pharmaceutical companies continually face many challenges in areas such as data collection, data validation, data harmonization, and data understanding due to the shifting landscape towards patient-centric trials, decentralized trials, reduced clinical cycle times leading to faster submissions. , political scenarios and pandemics. Today, data analytics is used not only to provide the best research directions and methodologies, but also to recruit the most eligible patients into a study that can provide the best clinical insights, thereby generating meaningful insights and providing the right medicine to as many people as possible. diverse population.

Data insights through predictive analytics, natural language processing, and machine learning
The latest data science contribution to research relates to natural language processing (NLP), machine learning and predictive analytics in clinical trials. NLP is one of the best breakthroughs in clinical research in which data is captured in the respective segments when the patient is talking to the computer. NLP has been able to segment data from various patients based on their medical records and pathology reports or direct medical information about patients from doctors’ clinical notes.

Predictive analytics helps extract useful insights from large clinical trial datasets, trends and associations where there are many variables, resulting in better data understanding and decision making effective. Data Scientists use predictive models based on machine learning algorithms to verify risk indicators thereby ensuring patient safety and also to check trial progress against primary and secondary objectives.

Some of the outcomes that predictive analytics and machine learning algorithms have helped in clinical areas are:

  • Predict clinical trial outcomes: Provide information about which patients will respond and how to a particular treatment based on their genetic makeup, age, medical history, and other characteristics. Predictive analytics can also be used to detect adverse events during clinical trials by analyzing real-world evidence such as electronic health records and insurance claims data in addition to clinical studies.
  • Predicting medication side effects: using data analysis to predict which group of patients in the study population will be most likely to experience certain side effects.
  • Drug Interaction Prediction: Predictive modeling/machine learning can be used to extract information about adverse events that might occur when two or more drugs are administered together to the patient. It can also help identify low-risk interactions through analysis of available virtual patient models before actually using the drugs in humans.
  • Predict clinical trial enrollment: Using clinical data, machine learning modeling techniques can be used to predict the groups of patients (responders) most likely to enroll in clinical trials.
  • Predict clinical trial dropout rates (non-responders): Predictive modeling/machine learning can be used to predict clinical study completion or the likelihood that a clinical participant will complete the full course of treatment.

Therefore, with the world of research moving faster than ever, there is an unprecedented speed to move a molecule from the lab to the shelf and thus deliver the much-needed benefits to patients and meet their unmet medical needs. . As data becomes accessible and interoperable and artificial intelligence becomes more pervasive in research, providing international collaborations through connected data is also accelerating research.

Although data scientists are developing predictive models and machine learning algorithms, the need for human intervention to carefully analyze and interpret the usefulness of data is essential to further develop data science.

Ram Mudaliar, Senior Director (Head of CDI India), Clinical Data & Insights (CDI) at AstraZeneca

(DISCLAIMER: The views expressed are solely those of the author and ETHealthworld does not necessarily endorse them. ETHealthworld.com shall not be liable for any damage caused to any person/organization directly or indirectly.)

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