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From drug discovery to clinical research and commercialization: The Partnerships, alliances, and collaborations
The industry, across the drug development continuum, has so far this year announced myriad new partnerships, strategic alliances, product launches, and reports.
Investment in AI for drug discovery startups, specifically, increased from $200m in 2015 to more than $700m in 2018 – and the number of companies in this space increased by 20, according to a report published by Deep Knowledge Analytics, the analytical arm and subsidiary of Deep Knowledge Ventures.
A big player in the drug discovery space, Insilico Medicine also recently developed a semi-automated contracting system to ease the collaboration process between AI companies and industry partners.
Among the deals focused on drug discovery efforts so far this year, Janssen and Iktos Pharmaceuticals in May entered an agreement to bolster small molecule discovery using a deep learning generative model.
Additionally, Charles River and Atomwise formed a strategic alliance aimed at accelerating the drug discovery process using AI-powered design technology.
Atomwise also inked a multi-year agreement with Eli Lilly and Company through which it will apply its patented artificial intelligence technology to support Lilly’s preclinical drug discovery efforts.
To aide protocol design using machine learning, Bristol-Myers Squibb entered a multi-year agreement with Concerto HealthAI, which also has a partnership agreement in place with Pfizer to advance precision oncology using real-world data. As part of this work, Concerto HealthAI launched a model to predict survival rates in lung cancer patients to garner disease insights that could improve enrollment criteria.
PPD’s agreement with China-based Happy Life Tech focuses on site selection, patient recruitment, and real-world evidence generation for customers globally. The agreement, announced in February, brings together HLT’s data and AI technology with PPD’s clinical trials and real-world evidence (RWE) generation abilities.
Worldwide Clinical Trials also formed a strategic alliance with Deep Lens, an AI-driven pathology company, which closed a $14m Series A financing round in April of this year.
Dave Bowser, executive vice president and general manager of the global clinical development division, Worldwide Clinical Trials, told us at the time that the goal is to enhance patient recruitment using near real-time diagnosis to alert the patient as well as the research and care teams.
Several companies also have launched new technology over the last eight months, with others securing funding to advance existing platforms.
To support drug discovery and development, Recursion Pharmaceuticals plans to partner with big pharma on rare disease programs after raising $121m to build out its machine learning-enabled platform.
Clinithink – after setting Guinness World Record for the fastest genetic diagnosis – earlier this year secured investors to scale its technology, which leverages unstructured data to guide treatment decisions, identify clinical trial participants, and ensure proper reimbursement. The company this month was selected to better manage clinical data at NHS GDE Trust Worcestershire Health and Care, using its natural language processing platform.
Additionally, Saama Technologies in March closed a $40m financing with Perceptive Advisors, a NY-based firm that focuses on supporting the life sciences industry.
Saama CEO Suresh Katta said the new funding will be used to further invest in and expand the company’s Life Science Analytics Cloud (LSAC). The platform is powered by AI to support clinical trial design and conduct across various development stages, according to the company, which has raised $75m since 2015.
Accenture also launched a new AI tool to improve connectivity between researchers. The platform, dubbed Intient, provides access to clinical data and links companies working on drug delivery, using AI to enable the clients to store and process their data, as well as to share across the company's collaborative teams.
To steer the implementation of principles for data management and stewardship, which it said are required as the industry undertakes a digital transformation, the Pistoia Alliance in July launched a toolkit to build better AI, machine learning, and support the Lab of the Future.
Still, while there are several concerns about the implementation of AI, regulators across the globe are pushing for innovation.
According to a study published earlier this year, AI threatens data privacy by making it possible to re-identify individuals using their physical activity data. Per the report, published by the University of California, Berkeley, current laws and regulations fail to safeguard confidential health information.
However, global regulators are pushing for innovation with several agencies outlining plans to explore the use of new digital technologies.
Addressing “the promise of AI,” Mick Foy, head of pharmacovigilance strategy, vigilance intelligence, and research group, Medicines and Healthcare Products Regulatory Agency (MHRA), said it’s a promise the agency is “very much looking forward to.”
The European Medicines Agency (EMA) also this year released its core recommendations for driving collaborative evidence generation and improving the scientific quality of evaluations. This includes exploiting digital technology and AI in decision making.
Laying the groundwork for new approaches ‘to foster innovation in digital health’ in the US, the Food and Drug Administration (FDA) published its Digital Health Innovation Action Plan.
The challenges of successfully using AI are not to be diminished, however, said Bristol-Myers Squibb’s head of clinical trial analytics Balazs Flink, MD, at the SCOPE Summit earlier this year.
“AI works best in a relatively standardized, stable environment,” said Flink. “We are not there yet.”