Hemanth Kanakamedala, The Janssen Pharmaceutical Companies of Johnson & Johnson
As we continue to better understand immune-mediated inflammatory diseases (IMIDs) and develop new solutions for patients’ treatment needs, especially for patients with rare diseases or those with major but difficult-to-treat conditions such as psoriatic arthritis (PsA). ), including rheumatoid arthritis (RA) and inflammatory bowel disease (IBD), Crohn’s disease (CD) and ulcerative colitis (UC) – Janssen’s Immunology and R&D Data Science teams using real-world data (RWD) they are driving. impact throughout the drug development life cycle.
We apply machine learning and artificial intelligence (AI) to real-world data (RWD) – including administrative claims, electronic health records, laboratory data, disease registries – to generate evidence about diagnosis, prognosis and etiology. This is crucial to provide the right context for the appropriate use of new and existing therapies. This real-world evidence (RWE) gives us insight into patients’ medical needs, their journeys, gaps in current treatment options, and new areas of research. Some specific areas of influence are highlighted below.
RWD Is Proving Invaluable For The Identification And Development Of Biomarkers
RWD – powered by data science – helps us better understand the diseases we are dealing with and the patients affected by them. At Janssen, we are using RWD to generate accurate phenotypic profiles that provide a comprehensive analysis of patients’ clinical characteristics and their immunological diseases. Not only is the assessment of what is documented in the patients’ medical records, but also their labs and/or imaging. This information helps us develop more accurate disease classification systems using AI and NLP, which brings us one step closer to precision medicine.1 This helps us identify new disease biomarkers, discover promising compounds that can target them, and advance them clinically. .
Use Of Data Science And RWD To Design Smarter, More Efficient, And More Representative Clinical Trials
Knowing the natural history of a disease is critical to drug development. Read also : In a time of global problems, it is becoming more difficult for researchers to collaborate across national borders – New Hampshire Bulletin. We use RWD to inform the design of our intervention studies, including inclusion/exclusion criteria, diagnostic criteria, appropriate follow-up, hypotheses driving the studies, and other design components.
RWD is particularly important for drug development in ultra-rare immune diseases, such as hemolytic disease of the fetus and newborn (HDFN), a condition that occurs during pregnancy when maternal red blood cells or blood group antibodies cross the placenta and destroy fetal red blood cells. . Randomized controlled trials are often impractical and unethical in such populations. As a result, such patient populations have historically been underserved by traditional clinical development programs. We enroll such patients in single-arm studies with real-world external control arms using rigorous methods to control confounders.
Recruiting patients for our studies that reflect the same real-world characteristics is fundamental to the successful treatment of immune-mediated disease. Diversity, equity and inclusion embedded in study inclusion/exclusion criteria and active patient recruitment are critical to meeting the needs of all patients and enable our ability to improve access to innovative therapies. Without including all subpopulations of patients with immune-mediated disease, it is difficult for researchers to fully understand disease progression and response to therapy in important patient subgroups.
This is especially important in underserved and underserved populations. We constantly ask ourselves if our clinical trial sites are in the right places and if we are making our clinical trials accessible to all immune disease patient populations. With that in mind, Janssen is applying AI and machine learning to RWD to help identify pockets of patients with rare or hard-to-diagnose diseases and inform the location of study sites, with the goal of enabling communities of patients who may not have them. to enroll in a study that participated in a clinical trial in the past.
We know that diseases and drugs can affect people differently based on race and ethnicity, so matching clinical trial enrollment with patient population demographics is critical. Simple but impactful decisions, such as ensuring that clinical trial sites are located in accessible locations in historically underserved communities, make a big difference in our ability to reach a representative population to ensure that we are learning all about how our new therapies treat the medically underserved. need in all races, ethnicities and genders.
Integrating Digital, Real-World Endpoints Into Trials
Understanding how improvement would be observed and measured in a real-world clinical setting is critical to advancing outcomes among patients with diseases such as CD and other IMIDs. To see also : Halo Infinite multiplayer reportedly causes players to use 1GB of data per game. Important RWDs such as endoscopy videos and histology slides – using computer vision algorithms to measure disease severity – are incorporated into our CD clinical trials and create a bridge between the results of standard clinical trials and measures assessed in the real-world clinical setting.
The RWE approach allows us to collect more comprehensive data so that we can contextualize the results of randomized controlled trials (RCTs) to questions about diagnosis, prognosis and disease etiology. The answers to these questions are also critical to indicating the value of changing health outcomes.
Comparative Effectiveness Research After Product Launch
Tokenized RWE also helps us generate evidence about healthcare resource utilization and other real-world outcomes during and after the completion of our trials. After launch, we monitor the effectiveness and safety of our products through RWD’s analysis. Read also : Children receive colorful lessons at the Imagine That science camp. To mitigate the limitations of traditional case-control designs, we mimic pragmatic RCTs of approved treatments2. This type of comparative evidence generation is critical to informing the real-world effectiveness of our therapies.
RWE plays an increasingly important role in the product lifecycle in our immunology assays. To learn more about our work, visit our Immunology and R&D Data Science sites.
Hemanth Kanakamedala is the chief immunologist at Janssen R&D Data Sciences. His expertise lies in drawing causal inferences using observational and non-random data. His work focuses on externally controlled interventional trials, emulating randomized experiments using observational data and integrating patient-centered digital health endpoints into trials. Prior to joining Janssen, Kanakamedala spent 10 years helping to design and execute phase 1-3 randomized controlled trials and non-interventional studies. He holds a BA in Mathematics and Statistics from the University of Massachusetts, Amherst.
Artificial intelligence (AI) is said to be the next big thing in technology. And we also think Big Data is the next big thing.
Is being a data scientist stressful?
Data scientists often have to put in long hours, especially when working to solve a big problem. But the field has become very competitive in recent years, and the sheer level of competition can be stressful. This requires staying ahead of the competition and improving further.
Is it hard to be a data scientist? Because of the often technical requirements for Data Science jobs, it can be more difficult to learn than other areas of technology. Gaining a solid handle on a wide variety of languages and applications involves a fairly steep learning curve.
Is it boring being a data scientist?
Being a data scientist is not what it is cracked up to be. It has its boring and repetitive tasks. According to a new survey, average data scientists spend more than half their time (53 percent) doing things they don’t dig into, such as cleaning and organizing data for analysis.
Is data scientist a low stress job?
Data scientist is arguably the least stressful of the three. If you work as a data scientist in an organization where data science is separate from engineering responsibilities, then you avoid the most stressful part of a software and data engineer’s job: making sure systems are up and running at all times.
Is data science major hard?
Data science courses are getting more and more difficult every year. 100-level introductory data science courses can be easy. However, 300- and 400-level courses will be tough stuff for juniors or seniors. Data science graduate courses are, unsurprisingly, the most challenging.
Is data science easy for beginners? Like any other field, with the right guidance, Data Science can become an easy field to learn, and one can build a career in it. However, because it’s so vast, it’s easy for a beginner to lose sight of it and get lost, making it a difficult and frustrating learning experience.
Is it worth getting a data science degree?
If you want a dynamic, high-paying job in an in-demand and fast-growing field, data science may be worth exploring. According to the US Bureau of Labor Statistics, job prospects for data scientists are expected to grow 22% from 2020 to 2030, much faster than the average for all occupations.
Is data science a fun major?
Data science can be a lot of fun… if data science is a weird job where you can do all the cool stuff together: math, coding, and research. A job where you can read a research paper in the morning, write an algorithm in the afternoon, and code in the afternoon. It’s really fun!
Does data analyst require coding?
Data analysts don’t need to have advanced coding skills either. Instead, they should have experience using analysis software, data visualization software, and data management programs. As with most data careers, data analysts must have high-quality math skills.
Can you do data analysis without coding? Yes, it is possible to become a Data Analyst without any coding skills. All that is required is good statistical knowledge, a solid grasp of mathematical formulas and impressive communication skills.
What are requirements for data analyst?
Essential Skills for Data Analysts
- SQL. SQL, or Structured Query Language, is the industry standard ubiquitous database language and is probably the most important skill for data analysts to know. …
- Microsoft Excel. …
- Critical thinking. …
- R or Pythonâ Statistical programming. …
- Data visualization. …
- Presentation skills. …
- Machine Learning.
