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AI plays critical role in pharmaceutical industry

Dr Vikas Pandey
Thursday, November 24, 2022, 08:00 Hrs  [IST]

Artificial Intelligence (AI), the simulation of human intelligence processes by machines, especially computer systems, has revolutionised all industries leading to the invention of different things like virtual assistants, self-driving cars, smart homes, chatbots, surgical bots, medicines and so much more.

According to Tractica, the global AI software market is forecast to grow from $10.1 billion in 2018 to $126 billion by 2025. In a data-driven age where companies across all parallels of the industry are adopting Big Data and AI technologies, the pharmaceutical industry is no exception. When it comes to the pharmaceutical industry, AI presents an ocean of untapped opportunities for business transformation. AI can significantly improve the value proposition of pharma companies by driving innovation and creation of new business models.

AI applications in pharma industry
AI can be implemented in almost every aspect of the pharmaceutical industry, right from drug discovery and development to manufacturing and marketing. By leveraging and implementing AI systems in the core workflows, pharma companies can make all business operations efficient, cost-effective and hassle-free.

AI is used in the pharma industry to manage the process of clinical trial databases, it manages all the clinical trials database and maintain, organise and store the data in a database. AI is also used to reduce cost of developing new drugs, it also helps to improve the services of clinical research services to take better decisions, manage the database and increase revenue. AI is being used in the pharmaceutical industry to help in the processes of drug discovery and development. The first use of AI in the pharmaceutical industry was by Pfizer.

AI involves several method domains, such as reasoning, knowledge representation, solution search, and, among them, a fundamental paradigm of machine learning (ML). ML uses algorithms that can recognise patterns within a set of data that has been further classified. A subfield of the ML is deep learning (DL), which engages artificial neural networks (ANNs).

AI plays a critical role in the pharmaceutical industry. most mention-worthy applications are drug discovery and development, drug repurposing,drug manufacturing, diagnostic assistance, disease prevention, rare diseases and personalised medicine, epidemic prediction, remote monitoring, optimising medical treatment processes, identifying clinical trial candidates manufacturing and marketing, according to industry stakeholders.

Atomwise and Sanofi have entered a strategic and exclusive research partnership to use the former’s AI-driven AtomNet platform to discover and research up to five drug targets computationally. The approach of Atomwise makes the process of drug.

Future of AI in pharma industry
Nearly 50% of global healthcare companies will implement AI strategies and by 2025 and some experts believe it is crucial for how businesses operate down the line.Top pharmaceutical companies are collaborating with AI vendors and leveraging AI technology in their manufacturing processes for research and development and overall drug discovery. In fact, reports show nearly 62 per cent of healthcare organisations are thinking of investing in AI in the near future, and 72 per cent of companies believe AI will be crucial to how they do business in the future.

The recent surge in activity in deploying AI capabilities in the pharmaceutical industry shows no sign of slowing down. According to recent research, about 50 per cent of global healthcare companies plan to implement AI strategies and broadly adopt the technology by 2025.

Specifically, global pharmaceutical and drug development companies will invest more in discovering new drugs for chronic and oncology diseases. Chronic diseases are the leading causes of death in the US. Therefore, organisations are increasingly leveraging AI to improve chronic disease management, drive down costs, and enhance patient health.

Some of the major chronic diseases that AI will tackle in the future include chronic kidney disease, diabetes, cancer, and idiopathic pulmonary fibrosis. AI will shape the future of pharmaceuticals by improving candidate selection processes for clinical trials. By quickly analysing patients and identifying the best patients for a given trial, AI helps ensure uptake by providing trial opportunities to the most suitable candidates. It also helps to remove elements that may hinder clinical trials, reducing the need to compensate for those factors with a large trial group.

Organisations will also continue to leverage AI to better screen and diagnose patients. Experts can use AI to extract more valuable information from data that already exists, including in MRI images and mammograms.As AI tools become more accessible over the years, they will become part of the natural process within pharmaceutical and manufacturing. The future will be AI-enabled.

AI in pharma to predict what’s possible and translate it into business performance -- from molecule to market.AI, analytics, and data technologies help to identify unique advantages with greater accuracy, bring urgently needed therapies to market faster, and more effectively reach patients. Its use reduces human workload as well as achieving targets in a short period of time.

CortexAI is a cloud-agnostic AI development platform with plug-and-play technologies which are used for the data, analytics, intelligent automation, and machine learning, thus help pharma companies weigh ethical considerations, responsibility, assurance, and accountability.

To conclude, the scope of AI in the pharmaceutical industry looks highly promising. As an increasing number of pharma companies adopt AI technologies, it will lead to the democratisation of these advanced technologies, thereby making it more accessible for small- and medium-sized pharma companies as well. The AI will help to create new drug combinations and invention which will advance the effectiveness and cost of medications.    

(The author is associate professor, Amity Institute of Pharmacy, Amity University Madhya Pradesh, Gwalior)


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