The growing demand for ML/AI technologies, as well as for ML/AI talent, in the pharmaceutical industry is driving the formation of a new interdisciplinary field: data-driven drug discovery/healthcare. Consequently, there is a growing number of AI driven startups offering technology solutions for drug discovery/development. In drug development, preclinical phase (in vitro and in vivo), also named preclinical studies and nonclinical studies, is a stage of research that begins before clinical trials, and during which important feasibility, iterative testing and drug safety data are collected. According to a detailed mind-map prepared by Pharma Division of Deep Knowledge Analytics (updated Q1 2019): the AI for Drug Discovery, Biomarker Development and Advanced R&D Industry Landscape counts so far 400 investors, 170 companies and 50 corporations. This article focuses only on the AI startups and the AI investors trying to overcome the above 4 challenges during design and execution of the preclinical phase.
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.
The biomedical startup was founded by University of Toronto alumni David Q. Chen, Elvis Wianda, Liran Belenzon, Tom Leung.So far the venture has raised US$8 million, contributed by a group of investors including Montreal's iNovia Capital and Google's Gradient Ventures (which is Alphabet's AI venture capital firm). The new company is called BenchSci and it aims to use artificial intelligence to scan through millions of data points, drawn from published research papers, in order to find new compounds that can help to accelerate the drug discovery process. The focus of the new venture is with finding commercial antibodies. The researchers spent two years building machine learning software that can extract antibody usage data from published figures. This involves decoding millions of papers, with the end result of making the data easily discoverable for scientists.
Artificial Intelligence (AI) has been a top trend in many industries lately, attracting massive media attention and investments. Over the last decade, this complex area of research has rapidly progressed from being a "resurrected cool technology from the past" to a full-blown driver of nothing less than a new industrial revolution -- a digital one. As of today, AI is widely commercialized in such applications as manufacturing robots, smart assistants (e.g. Siri), automated financial investing systems, virtual travel booking agents, social media monitoring tools, conversational bots, surveillance systems, online security systems, language translators, self-driving cars, and much more. In some industries, AI (including its many technologies and sub-disciplines, such as deep learning, recommender systems, and natural language processing), is becoming a standardized component rather than a cutting-edge innovation it once was. This rapid progress in AI adoption is also seen in the pharmaceutical industry -- not without caveats, however. Unlike "mainstream" use cases, like image recognition or spam email filtering, drug discovery research appears to be a much harder case for several reasons.