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AI for Pharma

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Linda Kallfa

The COVID-19 pandemic has sped up the adoption of artificial intelligence (AI) in the pharma market. An increasing number of pharmaceutical companies are applying AI, shaping the future of the technology in the industry. Top pharma companies are collaborating with AI vendors and using AI technology in their manufacturing processes for research and development (R&D) and drug discovery. In fact, reports show that nearly 62% of healthcare organizations are thinking of investing in AI, and 72% of companies believe AI will be crucial to how they do business in the future.

How AI is currently used in Pharma

It’s estimated that AI and machine learning (ML) in the pharmaceutical industry could generate nearly $100 billion annually across the U.S. healthcare system. According to the researchers, AI and ML improve decision-making, enhance innovation, improve efficiency in drug discovery and clinical trials, and create new tools for consumers, regulators, insurers, and physicians.

Top pharmaceutical companies, including Roche, Pfizer, Merck, AstraZeneca, GSK, Sanofi, AbbVie, Bristol Myers Squibb, and Johnson & Johnson, have already collaborated with, or acquired AI companies. 

In 2018, the Massachusetts Institute of Technology (MIT) partnered with Novartis and Pfizer to transform drug design and manufacturing through the Machine Learning for Pharmaceutical Drug Discovery and Synthesis consortium. The consortium aims to break down the divide between ML research at MIT and drug discovery by bringing researchers and industry together. 

That same year, GSK started collaborating with Cloud Pharmaceuticals, an AI-driven drug design and development company, to accelerate the discovery of novel small-molecule candidates.

In response to the COVID-19 pandemic, in April 2020, GSK and Vir Biotechnology began partnering to use CRISPR and AI technology to identify antiviral compounds that can treat coronaviruses, including COVID-19. A few months later, Roche partnered with Owkin, an ML platform for medical research, to speed up drug discovery, development, and clinical trials. 

And most recently, Abbott launched a coronary imaging platform powered by AI. The platform can detect the severity of calcium-based blockages and measure vessel diameter to boost the precision of decision-making during coronary stenting procedures.

Without a doubt, we’re witnessing a revolution in the pharmaceutical and healthcare industries. AI is enabling researchers to discover new treatments faster than what was possible even a decade ago.

Best use cases for AI

AI and ML play a critical role in the pharmaceutical industry. However, the best use cases are drug discovery, clinical trials, real-world data, drug manufacturing, diagnostic assistance, and optimizing the therapeutic treatment process.

The process of developing a drug from preclinical research to marketing can take 12 to 18 years. It often costs between $2 billion and $3 billion, and only about 10% of candidates successfully complete clinical trials and gain regulatory approval. This expensive and competitive drug development process has motivated pharmaceutical companies to investigate AI as a new method to reduce R&D costs, while being compliant and avoiding errors. AI has the potential to transform the end-to-end drug development and delivery timeline, which could make medicines more affordable and increase the probability of FDA approval.

The technology companies can also help with the repurposing of new drugs. AI and ML algorithms can identify molecules that might have failed in clinical trials and predict how the same compounds could be applied to target other diseases.

In drug manufacturing, AI provides various opportunities to improve processes. For example, AI can perform quality control, improve production reuse, perform predictive maintenance, and reduce material waste. ML can help forecast and prevent overdemand and underdemand, and it can help fix supply chain problems and failures in the production line. 

AI and ML can assist with diagnoses by providing a data-driven approach to categorizing patients. When physicians diagnose patients, they look at symptoms, diagnostics tests, and historical data. Based on that information, a physician provides a patient with personalized treatment options.

Over the years, the FDA has approved dozens of AI platforms for personalized patient care. Some of the platforms were used for remote patient monitoring, others for recognizing abnormal heart rhythms on an Apple watch, and some other platforms were used to identify brain bleeding on a CT scan.

AI can help enhance the medical treatment process through mobile apps that measure and monitor health remotely. The personalized data from the apps can also help improve research and development, as well as treatment efficacy.

Why NetApp?

AI plays a significant role in the adoption of cloud solutions. AI has moved beyond prototyping: Companies worldwide are now using it in the execution and implementation phase. At NetApp®, we have more than 25 years of leadership experience in data management, so our pharmaceutical clients can be confident that data is protected, and access is compliant. Pharma companies can speed insights and innovation with scalable, GPU-powered architectures that are designed and optimized for AI. They can build a seamless AI pipeline no matter where the data lives, or where it moves to—edge, core, or cloud.

To learn more, visit our Artificial intelligence in medicine page, where you can find solution briefs, white papers, and customer stories, or contact our AI specialists.

Linda Kallfa

Linda leads the Life Sciences practice within the Healthcare and Life Sciences team. She brings her wealth of Pharmaceuticals and Life Sciences experience to conversations with customers, connecting the value of NetApp product and services to the line of business with focus on drug discovery, virtual and hybrid clinical trials, image analytics, digital, and beyond the pill initiatives. With 15 years of global experience, Linda started her career at GlaxoSmithKline R&D working as an innovation scientist at their Centre of Excellence. She then managed the clinical development of various brands while working in partnership with global commercial teams. 

Wanting to be part of the digital transformation in the pharmaceutical industry, Linda joined Medidata, a pioneering clinical research and data management company where she helped customers across EMEA adopt the Medidata Rave platform. Linda then joined QAD, an end-to-end ERP provider, as a business development executive in their Life Sciences business unit. She advised pharma and medical devices clients on ERP solutions for their quality, supply chain, and digital manufacturing departments. In partnership with solution marketing Linda started an early ERP adoption program for Cell and Gene manufacturers. Linda is a member of Healthcare Businesswomen`s Association (HBA).

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