Where can Artificial Intelligence and Machine Learning really drive change in pharma's business?
Artificial Intelligence (AI) and Machine Learning (ML) have the potential to unlock new insights and massively improve research and business processes across pharma's business. As the hype subsides, experts caution that a realistic understanding of what AI and ML can deliver for pharma will better channel investment and effort. Where is AI and ML best applied? How can pharma ensure that its AI programs are fit for purpose? How much can you reasonably expect your AI program to deliver?
In Artificial Intelligence and Machine Learning: Applications in Pharma we interviewed leading AI and ML experts to help you identify practical applications and evaluate where AI and ML could drive transformational change in your organization.
Experts scan the AI and ML horizon
- How can AI and ML technologies accelerate pre-clinical research and rapidly deliver viable drug development candidates?
- What can AI and ML do to identify new indications for your existing drug portfolio?
- In what ways can AI and ML help in selecting patients for clinical trials?
- Internal or external: when should pharma develop its own AI and ML competences and when should it engage external providers?
- Why–and how–should you tackle "dirty" data?
- How can AI and ML technology be employed to unlock insights from multiple data sources that will improve physician and payer confidence?
What AI and ML experts say…
"Clinical trial design–there are a number of firms applying AI in clinical trials to analyse the results of trials. For example, a trial that failed in Phase III may have inside of it a sub-trial that passes the Phase III with a patient that has specific biomarkers, so maybe you don't have to fail the entire program going for a blockbuster if you have the data on the patients that allows you to subset them."
"When it comes to AI and drug discovery there are three different models. The first is collaboration for services, whereby somebody else needs your expertise to develop their drug. The second business model is drug development; a lot of the smaller companies are developing their own drugs. The third business model that has not really transcended the market yet is one of selling subscriptions to data, and I think that will eventually be a big market breaker. But to win in the marketplace you have to have quality data."
"Pharma need to be asking the right questions, to have the right data with the right question. That's why you do need to have people who have the expertise to know what questions to ask, and at the same time you need to make sure that the data of which you are going to ask that question is in the right format and is of the right standard to give you an answer."
What to expect
A detailed primary research report, supported with case studies, exploring how AI and ML technology can radically improve pharma's drug development, clinical research, patient knowledge and stakeholder support programs.
- An examination of 5 key issues that pharma needs to understand and respond to
- 22 targeted questions put to AI and ML experts
- Their perceptive responses that provided 25 current insights supported by 67 directly quoted comments
The report harnesses critical insights from front line industry experts who completely understand AI and ML technology and the key areas where pharma can benefit.
- Ed Addison, Chairman and CEO of Cloud Pharmaceuticals, founder, adviser and serial entrepreneur of disruptive new ventures, speaker, author and professor, Raleigh-Durham, NC, USA
- Jackie Hunter, CEO, Benevolent AI, Cambridge, UK Benevolent. She directs the application of Benevolent's AI technology for drug development and give the company the insight it needs to operate its unique business model – one which sees it not only researching, but also developing the blueprint for new drugs.
- Rafael Depetris, PhD, Principal Scientist I, Kadmon Corporation, US. Rafael is dedicated to understanding the mechanism of action of small molecules and therapeutic proteins, and interested in the application of structural analysis in the improvement of AI, specifically focusing on machine learning methods for the prediction of affinity.
Why choose FirstWord FutureViews reports?
FirstWord's FutureViews reports analyse in detail significant emerging technology and market trends that pharma executives need to understand if they are to manage the opportunities and challenges that lay ahead. These concise and highly focused reports:
- Are based on primary research with experts whose knowledge and current experience is proven
- Present clear expert insights free from secondary source information and spurious observations
- Include only latest research and content–we don't reuse or recycle content