Too much of it kills you." A new report by Data Bridge Market Research analyses the global Artificial Intelligence (AI) in drug discovery market and forecasts that is expected to reach the value of USD 24,618.25 million by 2029, at a CAGR of 53.3% during the forecast period. The start of the world's first Phase 1 clinical trial of a drug developed from scratch using AI was announced by Insilico Medicine. "At the core of this issue is the complexity of human biology. After decades of molecular biology research, we're lucky if we know 5% of the circuitry of human disease." Just to give you a perspective, this is just the 5% of a simplified view of the brain's circuitry (for more about AI neuroscience news): Owkin's CEO Dr. T Clozel (his parents were the founders of Switzerland's Actelion), is intent on using AI to usher in a new era of drug development, by accessing data at scale with federated learning to preserve patient privacy and data security, and by creating an interpretable AI to answer a broad range of research questions. Last September rapid diagnostic solutions for breast and colorectal cancer from Owkin have been granted approval for use, while this month Sanofi's chief dealmaker (Alban de La Sablière) heads to Owkin as CBO, and all these after Owkin secured $80 million from Bristol Myers Squibb last year and a total raised to over $300 million. The Chicago-based Tempus (@TempusLabs), that specialises in AI and precision medicine and has one of the world's largest libraries of clinical and molecular data, announced this week it raised $275 million through equity from previous investors and debt financing from Ares Management (so far the company has raised over $1.3 billion). And also this month GSK announced that expanded its collaboration with Tempus to improve clinical trial design, speed up enrolment and identify drug targets. "This collaboration will provide GSK with unique insights to discover better medicines and transform drug discovery.

Artificial intelligence and other advanced analytical tools are increasingly popular with pharma firms and their research partners. Such technology can be useful for sorting through tall mountains of data to determine which candidates might offer hope to patients in need of a novel treatment for their particular condition. However, an AI-based approach might miss the mark by leaving out a human touch. David Harel, CEO and co-founder of Cytoreason, spoke with Outsourcing-Pharma about what tasks AI is best suited for, where it might fall short, and how to refine your approach toward drug discovery. OSP: Looking back on news OSP has shared about CytoReason, you've had an interesting few months.

Mati Gill's exposure to the power of pharma collaboration can't be overstated. During his 11 years as COO of the Global Legal Group, and later, head of government affairs, corporate & international markets at Teva Pharmaceuticals, he was exposed to virtually every aspect of the business. When Teva made a concerted effort to build its corporate innovation strategy to strengthen its development platforms and pipeline, he was an undisputed pick to lead the exercise in support of Teva R&D for the Israeli pharma titan. As he helped make inroads with Israeli's academic and emerging life sciences ecosystems, a promising opportunity began to reveal itself and became the focus of his work: The roles of computational biology and artificial intelligence (AI) in drug discovery and development. Gill found the concept nascent among the next generation of innovators but hampered by the pharmaceutical industry at large.

AbCellera and Gilead Sciences announced a new multitarget antibody discovery collaboration building on their previous infectious disease partnership from 2019. AbCellera is now starting to reap the fruits of its labor, as its successful antibody discovery programs earned the company $203 million in revenue in the first quarter of this year, including $178 million in milestones and royalties. Gilead Sciences also announced a very interesting partnership during the first half of 2021, this time with Gritstone Oncology to create a vaccine-based immunotherapy as a cure for HIV. Under the terms of the deal, Gilead is paying Gritstone $30 million upfront and a $30 million equity investment, and potentially an additional $725 million in regulatory and commercial milestones and royalties on net sales. BenevolentAI and AstraZeneca have been collaborating closely since 2019 to use AI and machine learning for the discovery and development of new treatments for chronic kidney disease (CKD) and idiopathic pulmonary fibrosis (IPF).

The developed world is on the cusp of turning the corner in the fight against COVID-19 thanks to the unprecedented effort to rapidly develop and distribute effective vaccines. Now technologists are hoping to take drug development to the next level, and AI will play a big role. One of the companies at the forefront of using machine learning and AI to develop drugs is CytoReason. The company helps pharmaceutical firms like Pfizer accelerate drug development by providing high resolution models of the human body that's infected with the disease that the drug companies are targeting. "If I told you that in 200 years, drugs would be developed in a computer, you would not be real surprised," said CytoReason CEO and founder David Harel.

CytoReason's unmatched proprietary and public data, cutting-edge machine learning technologies, unique biological models and unrivalled analysis delivers: Only by understanding what human cells are doing in response to a disease, and its treatment, can we really understand how to better impact that disease. CytoReason's mission is to simulate the cells that can stimulate discovery of: We have developed a unique machine-learning driven approach to "seeing" the cells that can make the difference in patients seeing a better life. The insights our approach generates, enable pharmaceutical and biotech companies to make the right decisions, at the right time, in the drug discovery and development programs that bring better therapies. The immune system is a cell-based system. Gene-based studies can tell us a certain amount, but not the whole story.

Clinical Informatics tells us that: "Every year in the U.S., approximately 2 million patients participate in roughly 3000 clinical trials; six million patients are needed to meet U.S. recruitment goals. Consequently, up to 90% of trials are delayed or over budget". Experts blame the lack of data available - to both patients and researchers - to explain why only 5% of cancer patients, for example, end up enrolling in clinical trials. A study from Carnegie Mellon University and Albert Ludwig University in Germany predicts that "AI could cut the cost of drug discovery by about 70%" and Krishna Yeshwant, general partner at Google Ventures, estimates "AI would cut (clinical trial) costs by 90 percent." Artificial intelligence seems like the perfect solution, but Zikria Syed writes in MedCityNews that "clinical trial technologies haven't changed much since the current categories -- clinical trial management systems, electronic data capture, and interactive voice response, -- were established in the late 1990s." A recent Deloitte study also that tells us "a number of clinical trial activities still use the same processes as in the 1990s." In a sector that is usually at the forefront of technology – biotechnology - it is hard to believe this is happening. I spoke to six innovators who were tackling the massive problem head on – scientists and entrepreneurs working to bring clinical trials to the people who need them – to find out what they are doing to solve the serious innovation problem. The list of people is impressive for the diversity of solutions they're offering to clinical trials: Anna Huyghues-Despointes, Head of Strategy, Owkin; Simon Smith, Chief Growth Officer, BenchSci; Leila Pirhaji, Founder & CEO, ReviveMed; Shai Shen-Orr, Founder, Cytoreason; and Daniel Jamieson, CEO Biorelate and Gunjan Bhardwaj, Founder & CEO, Innoplexus. Additionally we spoke to consultant Dr. Chrysanthi Ainali, Co-Founder Dignosis and Instructor for the KNect365 Learning Course AI & Real World Evidence for Clinical Trials to ask her thoughts on the specific challenges AI startups in clinical trials face. "Healthcare brings great challenges for a technology company. It is inherently conservative and risk averse - Hippocratic Oath: 'first, do no harm'" says Simon Smith, Chief Growth Officer at Benchsci.

Following a recent story with the top twenty companies in the area of AI drug development, this time we will make a flight over this new ecosystem looking for the bright lights of a rigorous developing market. In a recent article about new drugs approved, we found out that in the record year 2018, 61 new drugs were launched which means 20% more new pharmaceutical products compared with the previous record year 1996. It's no wonder if AI drug development realizes the promises, that we will witness an explosive growth in the coming years. Some of the 20 most prominent companies have some very fresh news that it is worth our attention. The most recent news for Atomwise is the formation of a strategic alliance with Charles River Laboratories International, Inc.

NEW YORK (GenomeWeb) – Informatics firm CytoReason is betting that its method of modeling immunological activity could be a boon to pharmaceutical companies that are looking to identify potential drug targets or diagnostic biomarkers from multi-omic and immune interaction data gleaned from private and proprietary sources.