AI Powered Pharmaceutical Industry in 2023

This News Covers

• Which pharma companies are using AI
• What are the top 3 or top 5 use cases of AI used by Pharma companies
• IBM's research in AI for Clinical Trials
• What are the potential expectations of doctors from AI
• How is NVIDIA empowering Pharma with AI?
• BiomedX and Sanofi's Partnership

Artificial Intelligence (AI) is revolutionizing the field of Life Sciences, particularly in the Pharmaceutical industry. The application of AI and Machine Learning (ML) algorithms is transforming various aspects of the industry, including commercial, supply chain, clinical, and pharmacovigilance areas.

Artificial Intelligence is revolutionizing the pharmaceutical industry, reshaping business models, optimizing supply chains, expediting clinical trials, and improving pharmacovigilance.

In the commercial sector, this technology is streamlining operations, enhancing product intelligence, and predicting market trends. The McKinsey Global Institute estimates that these advanced algorithms could generate nearly $100B annually across the US pharmaceutical industry, indicating the significant potential of these technologies.

Its role in optimizing the pharmaceutical supply chain is also noteworthy. It provides real-time data for full transparency, enabling visualization and analysis of performance along the entire supply value chain. This includes analytical testing steps, manufacturing and packaging of drugs, and their distribution, ensuring efficiency and accuracy at every stage.

In the realm of clinical trials, this technology is expediting the process by finding the right patients from various data sources. It's also being used to identify and validate genetic targets for drug development, design novel compounds, and expedite drug development. This leads to a more efficient way to analyze information relevant to the safety and efficacy of drugs.

Pharmacovigilance, the science of detecting, assessing, understanding, and preventing adverse effects or any other drug-related problems, is also benefiting from these advanced algorithms, which are being applied to automate case intake, extract drug-drug interactions, and predict the effect of a drug-drug interaction. This helps tackle complex and high-dimensional data, allowing for accurate predictions and precise classification of data points.

Which pharma companies are using AI

Here is the list of pharma companies that are using AI:

1. AstraZeneca: AstraZeneca is using data science and AI to increase productivity within research and development to find new ways to treat, prevent, or even cure diseases. They are leveraging AI to streamline their operations and enhance their research capabilities.
2. Bayer: Bayer is using AI to trigger a fundamental shift within the innovation paradigm in the pharmaceutical industry. They are using big data and advanced analytics, including AI, to enhance their research and development efforts.
3. Bristol Myers Squibb: Bristol Myers Squibb believes in the power of science to address the greatest healthcare challenges of our time. Today, that power is increasingly enabled and amplified by computer science and digital capabilities like artificial intelligence (AI) and machine learning.
4. GlaxoSmithKline: GlaxoSmithKline is using AI and ML to provide both anatomical and functional information used for development and discovery of novel biomarker/phenotypes and imaging endpoints in clinical trials.
5. Johnson & Johnson: Johnson & Johnson is leveraging AI in various aspects of its operations. However, specific details about their AI initiatives are not readily available in recent news articles.
6. Novartis: Novartis has partnered with Microsoft to significantly bolster its AI capabilities from research through commercialization. This partnership aims to accelerate the discovery and development of transformative medicines for patients worldwide.
7. Takeda Pharmaceutical: Takeda Pharmaceutical has entered into a five-year strategic agreement with Accenture and Amazon Web Services to accelerate its digital transformation, which includes the use of AI.
8. Pfizer: In April 2019, Pfizer joined with Concerto HealthAI to use AI and real-world data in oncology. The collaboration will conduct novel synthetic control arm and prospective Real World Data outcomes study designs for therapeutics that are both pre- and post-approval.
9. Roche: Roche is using AI to improve the efficiency of clinical trials and to accelerate the drug discovery process. They are also using AI to analyze large amounts of data to identify new potential drug targets.
10. Sanofi: Sanofi is leveraging AI to enhance its research and development efforts. They are using AI to analyze large amounts of data to identify potential new drug targets and to improve the efficiency of clinical trials.

What are the top 3 use cases of AI used by Pharma companies

1. Exscientia: Exscientia, a UK-based company, has pioneered AI in small-molecule drug design. They have expanded their AI-based platform to develop novel therapeutic antibodies through generative AI design. In early 2020, the company reported the first AI-designed drug candidate to enter clinical trials. Exscientia is collaborating with Bristol-Myers Squibb on a handful of drug candidates headed to the clinic. It has also partnered with Sanofi, GSK, and PathAI on drug discovery projects. The company is also working with MD Anderson to develop novel small-molecule oncology therapies.
2. Atomwise: Atomwise specializes in using AI in small molecule drug discovery. Atomwise has developed AtomNet, a deep learning-driven computational platform for structure-based drug design. The company's library includes more than three trillion synthesizable compounds. It is a member of the Alliance for Artificial Intelligence in Healthcare (AAIH), advocating for AI research and implementation in the pharma industry. Despite announcing a 30% headcount reduction in December 2022, the company has raised more than $194 million from investors and partnerships to date.
3. Recursion Pharmaceuticals: The clinical-stage biotechnology company Recursion, based in Salt Lake City, specializes in drug discovery through machine learning using its proprietary Recursion Operating System. Focusing on gene mutation-related diseases, the company claims to have one of the world's most extensive biological and chemical datasets. It now has several compounds in phase 1 and 2 studies, including a small molecule therapeutic for cavernous cerebral malformation and another for neurofibromatosis type 2. Recursion claims to conduct millions of experiments per week using supercomputers, machine learning, and automated robotic labs. The company went public in 2021.

These use cases highlight the potential of AI in accelerating drug discovery and development, improving the efficiency of clinical trials, and enhancing the understanding of disease biology.

IBM's research in AI for Clinical Trials

IBM's research in advanced algorithms for clinical trials is focused on finding new uses for existing drugs and therapeutics. They have developed sophisticated algorithms that can model clinical trials rapidly and efficiently. One of their recent studies published in 2023, showcased how these algorithms could revolutionize drug development by making it quicker and more efficient.

For instance, researchers used DeepMind's technology to create synthetic syringes that inject tumor-killing compounds directly into cells. The process, which usually takes years, was achieved in just 46 days. DeepMind has also predicted the shape of almost every known protein with remarkable accuracy, a critical step in drug development that used to take years of lab work.

Another major breakthrough came when a biotech company, AbSci, first created and validated de novo antibodies in silico using zero-shot generative algorithms. Traditionally, antibodies are created using preexisting antibodies or templates, which can be time-consuming. In silico methods can reduce this time from 6 years to almost 18-24 months.

IBM Consulting is partnering with AWS and leveraging Large Language Models (LLMs) on their generative automation platform, ATOM, to create industry-aware, life sciences domain-trained foundation models. These models generate first drafts of narrative documents to assist human teams. IBM Consulting has been driving a responsible and ethical approach to these advanced algorithms for more than five years now, mainly focused on explainability, fairness, robustness, transparency, and privacy. They are assisting several life sciences entities in deploying these technologies in a responsible and trustworthy manner across several functions.

What are the potential expectations of doctors from AI

Doctors have several expectations from AI in the medical field. Here are some of the key expectations:

A future where artificial intelligence (AI) plays a pivotal role in healthcare, will enhance our capabilities to address some of the most pressing challenges we face in the medical field.

One of the most significant expectations we have from AI is its potential to improve diagnosis.

By analyzing medical images, such as MRIs, CT scans, and PET scans, AI can aid us in detecting diseases at an earlier stage. This early detection can lead to more effective treatments, better patient outcomes, and potentially less invasive and costly care. The vast amount of data generated in the medical field can be overwhelming. AI's ability to manage and analyze this data effectively is a game-changer. It can help us make informed decisions about patient care, bringing all medical knowledge to bear in service of any case. Personalized treatment plans are another area where AI can make a significant impact. By analyzing a patient's medical history, genetic information, and lifestyle factors, AI can help us design treatment plans tailored to the individual's needs. This level of personalization could lead to more effective treatments and improved patient outcomes.

AI also has the potential to reduce our workload by automating routine tasks such as data entry and appointment scheduling. This automation will free up more time for us to focus on what matters most - patient care. In the realm of research and drug discovery, AI can accelerate the process by analyzing vast amounts of research data to identify potential new treatments and therapies. This could lead to breakthroughs in medicine and improve patient care.

Patient monitoring, especially for those with chronic conditions, can also be enhanced with AI. By analyzing data from wearable devices, AI can alert us to any significant changes in a patient's condition, allowing us to intervene promptly. However, as we embrace the potential of AI, we must also ensure its ethical and responsible use. The use of AI in healthcare should enhance patient care, not replace the human touch that is so vital in medicine. We must ensure that AI is used responsibly, with the best interests of our patients at heart.

These expectations reflect the potential of AI to transform healthcare, improving patient outcomes, and making healthcare more efficient and personalized. However, it's important to note that while AI has great potential, it also presents challenges, such as data privacy and security, that need to be addressed.

How is NVIDIA empowering Pharma with AI?

NVIDIA, a leading chipmaker, is making significant strides in empowering the pharmaceutical industry with AI. The company recently invested $50 million in Recursion Pharmaceuticals, a biotech firm specializing in AI-driven drug discovery. This investment aims to expedite the development of Recursion's AI models for drug discovery.

Recursion uses AI-powered models to identify and design new therapies, offering these models to other drugmakers, including Roche and Bayer. The company will use its extensive biological and chemical datasets, exceeding 23,000 terabytes, to train its AI models on NVIDIA's cloud platform. These datasets grow by hundreds of terabytes every week, providing a rich source of data for training AI models.

NVIDIA's cloud platform will be used to train these AI models, which can then potentially be licensed on Bionemo, NVIDIA's cloud service for generative AI in drug discovery. Recursion expects to use Bionemo to support its internal drug pipeline and those of its current and future partners.

NVIDIA's hardware that powers these AI models includes their high-performance GPUs (Graphics Processing Units), which are designed to handle the vast computational requirements of AI workloads. The specific hardware used can vary, but it typically includes NVIDIA's data center GPUs like the A100, which offers massive acceleration for AI and high-performance computing workloads. The A100 GPU features NVIDIA's Ampere architecture, with 6912 CUDA cores and 40GB or 80GB of high-speed HBM2 memory.

This partnership between NVIDIA and Recursion is a prime example of how AI is transforming the pharmaceutical industry. By leveraging NVIDIA's advanced AI capabilities and hardware, Recursion can accelerate its drug discovery process, potentially bringing life-saving treatments to patients faster.

BiomedX and Sanofi's Partnership

BioMed X and Sanofi have entered into a research partnership to leverage artificial intelligence (AI) for drug development. The collaboration aims to use digital data and AI to predict the efficacy of first-in-class drug candidates using virtual patient populations. This is a significant step forward given the current 90% failure rate of new drug candidates during clinical development.

Under the joint research project, a new research team will be set up at the BioMed X Institute in Heidelberg, Germany. The team will work on the co-creation of a next-generation virtual patient engine for clinical translation of drug candidates. This engine will simulate patient responses to potential new drugs, helping to identify promising candidates and reduce the risk of failure in later stages of development.

The partnership represents the first joint research project between Sanofi and the BioMed X Institute. It is an example of how pharmaceutical companies are increasingly turning to AI to improve the efficiency and success rate of drug development. By simulating patient responses to new drugs, the virtual patient engine could potentially save significant time and resources in the drug development process.

This partnership is part of a broader trend of pharmaceutical companies partnering with tech companies to leverage AI in drug discovery and development. With its advanced AI capabilities, BioMed X is well-positioned to assist Sanofi in its drug development efforts.