Artificial Intelligence for Clinical Trial Design

Abstract

Suboptimal patient selection and recruiting techniques, paired with the inability to monitor and coach patients effectively during clinical trials, are two of the main causes for high trial failure rates.High failure rates of clinical trials contribute substantially to the inefficiency of the drug development cycle, in other words the trend that fewer new drugs reach the market despite increasing pharma R&D investment. This trend has been observed for decades and is ongoing.AI techniques have advanced to a level of maturity that allows them to be employed under real-life conditions to assist human decision-makers.AI has the potential to transform key steps of clinical trial design from study preparation to execution towards improving trial success rates, thus lowering the pharma R&D burden. Clinical trials consume the latter half of the 10 to 15 year, 1.5–2.0 billion USD, development cycle for bringing a single new drug to market. Hence, a failed trial sinks not only the investment into the trial itself but also the preclinical development costs, rendering the loss per failed clinical trial at 800 million to 1.4 billion USD. Suboptimal patient cohort selection and recruiting techniques, paired with the inability to monitor patients effectively during trials, are two of the main causes for high trial failure rates: only one of 10 compounds entering a clinical trial reaches the market. We explain how recent advances in artificial intelligence (AI) can be used to reshape key steps of clinical trial design towards increasing trial success rates. Clinical trials consume the latter half of the 10 to 15 year, 1.5–2.0 billion USD, development cycle for bringing a single new drug to market. Hence, a failed trial sinks not only the investment into the trial itself but also the preclinical development costs, rendering the loss per failed clinical trial at 800 million to 1.4 billion USD. Suboptimal patient cohort selection and recruiting techniques, paired with the inability to monitor patients effectively during trials, are two of the main causes for high trial failure rates: only one of 10 compounds entering a clinical trial reaches the market. We explain how recent advances in artificial intelligence (AI) can be used to reshape key steps of clinical trial design towards increasing trial success rates. It takes on average 10–15 years and USD 1.5–2.0 billion to bring a new drug to market. Approximately half of this time and investment is consumed during the clinical trial phases of the drug development cycle. The remaining 50% of R&D expenditure covers preclinical compound discovery and testing, as well as regulatory processes (Figure 1). Although pharma and biotechnology companies have continuously increased R&D investment for decades, the number of new drugs gaining regulatory approval per billion USD spent has halved approximately every 9 years [1.Scannell J.W. et al.Diagnosing the decline in pharmaceutical R&D efficiency.Nat. Rev. Drug Discov. 2012; 11: 191-200Crossref PubMed Scopus (1252) Google Scholar]. Reversing Moore’s law (see Glossary) from the world of semiconductor technology, this trend has been termed Eroom’s Law. It is ongoingi [2.Thomas D.W. et al.Clinical Development Success Rates 2006–2015. BIO, Biomedtracker, and Amplion, 2016Google Scholar] and poses a severe threat to the existing clinical development business model: in the post-blockbuster drugs era a lack of go-to-market efficiency of that magnitude is not sustainable. One of the main stumbling blocks in the drug development pipeline is the high failure rate of clinical trials. Less than one third of all Phase II compounds advance to Phase III [3.Hay M. et al.Clinical development success rates for investigational drugs.Nat. Biotechnol. 2014; 32: 40-51Crossref PubMed Scopus (1489) Google Scholar]. More than one third of all Phase III compounds fail to advance to approval [4.Wong C.H. et al.Estimation of clinical trial success rates and related parameters.Biostatistics. 2019; 20: 273-286Crossref PubMed Scopus (580) Google Scholar]. Because these crucial checkpoints do not occur until far into the second half of the R&D cycle – with the most complex Phase III trials carrying ~60% of the overall trial costs (Figure 1) – the resulting loss per failed clinical trial lies in the order of 0.8–1.4 billion USDii, thus constituting a significant write-off of the total R&D investment. Two of the key factors causing a clinical trial to be unsuccessful are patient cohort selection and recruiting mechanisms which fail to bring the best suited patients to a trial in time, as well as a lack of technical infrastructure to cope with the complexity of running a trial – especially in its later phases – in the absence of reliable and efficient adherence control, patient monitoring, and clinical endpoint detection systems. AI (Box 1) can help to overcome these shortcomings of current clinical trial design. Machine learning (ML), and deep learning (DL) in particular (Box 2), are able to automatically find patterns of meaning in large datasets such as text, speech, or images. Natural language processing (NLP) can understand and correlate content in written or spoken language, and human–machine interfaces (HMIs) (Box 2) allow natural exchange of information between computers and humans. These capabilities can be used for correlating large and diverse datasets such as electronic health records (EHRs), medical literature, and trial databases for improved patient–trial matching and recruitment before a trial starts, as well as for monitoring patients automatically and continuously during the trial, thereby allowing improved adherence control and yielding more reliable and efficient endpoint assessment. In the following sections we highlight aspects of clinical trial design with immediate potential entry points for AI, and explain specific AI techniques of interest and how their application will improve trial performance (Figure 2, Key Figure).Box 1The Evolution of AIThe use of AI in medicine dates back to the early 1970s when expert systems such as MYCIN were first introduced to provide diagnostic decision support [48.Clancey W.J. Shortliffe E.H. Readings in Medical Artificial Intelligence: The First Decade. Addison-Wesley Longman, 1984Google Scholar]. However, early medical AI systems relied heavily on medical domain experts to train computers by encoding clinical knowledge as logic rules for specific clinical scenarios. Such systems suffered from the limitation that they were labor-intensive and time-consuming to construct, and once built they were rigid and difficult to update [49.McCauley N. Ala M. The use of expert systems in the healthcare industry.Inf. Manag. 1992; 22: 227-235Crossref Scopus (23) Google Scholar]. More advanced ML systems that are capable of training themselves to learn these rules by identifying and weighing relevant features from data such as unstructured text, medical images, and EHRs emerged in the 90s and 2000s, but were relatively slow to be adopted by the medical field, largely because of the lack of widely available data and the fact that the early methods required intense feature-engineering efforts involving serious commitments from medical domain experts [50.Niu F. et al.HOGWILD!: a lock-free approach to parallelizing stochastic gradient descent.arXiv. 2011; (Published online June 28, 2011. https://arxiv.org/abs/1106.5730)Google Scholar].This situation has changed dramatically recently because of two factors. First, the of AI itself in and related ML by and large training datasets et PubMed Scopus Google F. et learning in medicine – and Scopus Google Scholar]. medical data available in to new advances as well as efforts such as the in the years have a in efforts as well as early of AI in from medical for et and of a deep learning for detection of in PubMed Scopus Google Scholar] and et of with deep PubMed Scopus Google to the use of data to clinical from to the of human and artificial 2019; PubMed Scopus Google Scholar]. The of has also from this in AI methods at from natural language processing (NLP) of the et learning for information learning from for Google to et and knowledge from of with 2014; 20: to of of learning with and on Google and drug PubMed Scopus Google in of human intelligence processes and The of AI is to that can the world and in the as ML for between in large databases to help a to the and capabilities of the human from new a between or and to as a a or a learning a of ML methods on artificial by information processing and in that use to level features from The in to the number of which the data is learning learning is of ML that is with that can in as to of and to efficient to this a between a human and a artificial capable of automatically and to spoken or written human language a human–machine learning the study of that a of data to or to the ML is to be a of language processing a of AI with the between computers and human in particular how to computers to and large of natural language from and a of in AI, and at the electronic of of or into text, from a a of a a or from on The use of AI in medicine dates back to the early 1970s when expert systems such as MYCIN were first introduced to provide diagnostic decision support [48.Clancey W.J. Shortliffe E.H. Readings in Medical Artificial Intelligence: The First Decade. Addison-Wesley Longman, 1984Google Scholar]. However, early medical AI systems relied heavily on medical domain experts to train computers by encoding clinical knowledge as logic rules for specific clinical scenarios. Such systems suffered from the limitation that they were labor-intensive and time-consuming to construct, and once built they were rigid and difficult to update [49.McCauley N. Ala M. The use of expert systems in the healthcare industry.Inf. Manag. 1992; 22: 227-235Crossref Scopus (23) Google Scholar]. More advanced ML systems that are capable of training themselves to learn these rules by identifying and weighing relevant features from data such as unstructured text, medical images, and EHRs emerged in the 90s and 2000s, but were relatively slow to be adopted by the medical field, largely because of the lack of widely available data and the fact that the early methods required intense feature-engineering efforts involving serious commitments from medical domain experts [50.Niu F. et al.HOGWILD!: a lock-free approach to parallelizing stochastic gradient descent.arXiv. 2011; (Published online June 28, 2011. https://arxiv.org/abs/1106.5730)Google Scholar]. This situation has changed dramatically recently because of two factors. 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