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Identifying branded drugs with a low likelihood of generic entry has become a crucial strategy for companies looking to expand their product portfolio through in-licensing. In this comprehensive guide, we’ll explore the intricacies of identifying such drugs and leveraging them for successful in-licensing opportunities.
By leveraging research into specific genetic markers and tailored therapies, campaigns can address niche markets with unparalleled relevance. AI and BigData: Transforming Pharma Research Artificial intelligence and bigdata analytics are revolutionizing pharma research.
A data-mining study conducted by researchers in the US has found that an already-approved diuretic drug could have potential as a treatment for some patients with Alzheimer’s disease. The results are strong enough to back a proof-of-concept study in people with genetic risk of Alzheimer’s according to the researchers.
If you hadn’t already noticed, the clinical research enterprise has well and truly entered the era of “bigdata,” artificial intelligence (AI), and machine learning. Edited by Gary Cramer The post Recognizing the Real People Behind the BigData and Artificial Intelligence in Clinical Research appeared first on ACRP.
Alphabet subsidiary and precision health company Verily recently announced a breakthrough in its AI drug discovery GPCR research collaboration with Sosei Heptares. The companies hope that in the year to come those data targets will be entered for validation, hit generation, and lead selection. What, then, is the solution?
BigData in healthcare refers to the vast amount of data that is continuously expanding and cannot be efficiently stored or processed using traditional tools. It accounts for the majority of bigdata in healthcare and comprises information, such as medical images, surveys, chats, and written narratives.
With machine learning and bigdata analytics, companies can now predict market trends, enhance drug development, and create hyper-personalized campaigns. Bigdata analytics plays a crucial role in identifying prescribing patterns, understanding patient behaviors, and tailoring content strategies.
The repurposing of drugs is becoming more common, especially in the field of rare diseases. Now, as part of lifecycle management, pharmaceutical companies are looking more closely at drugs they have on their shelves. In the quest to repurpose a drug for a rare condition, there is a need to look at any and all available data.
online issue of Nature Communications, researchers at University of California San Diego School of Medicine describe a new approach that uses machine learning to hunt for disease targets and then predicts whether a drug is likely to receive FDA approval. the success rates in drug discovery?are biotech companies?have
HEPAprint – Adverse Drug Reaction (ADR). Clinical trials play a pivotal role in drug development. Understanding the possible scenarios relating to drug safety with the help of technology proves beneficial for all stakeholders. British startup HEPAprint develops predictive software to prevent adverse drug reactions.
AI-driven models also help in identifying the most effective treatment regimens based on patient-specific factors, including genetic makeup and treatment history. RELATED: OncoHealth Expands Its Iris Platform for Digital Cancer Care BigData and AI in Oncology Bigdata analytics is opening new avenues in cancer research.
The profits from these products funded the development of new drugs, such as gastroesophageal treatment Losec and cardiovascular treatment Aptin. However, it withdrew its neuropharmacological drug, Zelmid, which was an SSRI, due to concerns over side effects only a year after it was introduced in 1982. Zeneca Group plc.
Whether its new drug approvals like Ozempic or shifts in consumer behavior, the landscape is constantly evolving. Precision Medicine Marketing: Drugs like Keytruda , designed for specific genetic profiles, have revolutionized treatment approaches. Global Market Challenges: Marketing a drug like Humira in the U.S.
percent of the population (approximately 5 million people) are at increased risk of Type 2 Diabetes with an HbA1c ( as per the Diabetes UK, based on data from the Health Survey for England). Several genetic and lifestyle factors are observed to be the factors for the development of Type 2 diabetes. Similarly, in the UK, around 10.7
Having a strong background in biochemistry or genetics is necessary for the problem-solving element of the job. This job entails combining scientific information with computational information from databases to make sense of bigdata. This job furthers the field of biomedical research and supports drug development.
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Social media, online forums, and digital content are becoming primary channels for disseminating information about new drugs and therapies. This data-driven approach is helping companies optimize their marketing spend while maximizing impact and reach.
Researchers discovered a highly virulent variant of HIV in the Netherlands that genetic sequence analysis suggests has been circulating since the 1990s. . Led by researchers from the University of Oxford ‘s BigData Institute, the study underscores the significance of analysing viruses continually over time.
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