Excerpts from the book
Confronting Complexity
X-Events, Resilience, and Human Progress
by
John L. Casti
Roger D. Jones
Michael J. Pennock
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Bundled
The cost of sequencing an individual human genome is rapidly dropping below $1000. Much of the population can now easily access the details of their susceptibility and response to various disease states and conditions. Since a person’s genome is as individual as a fingerprint and every person’s response to a disease state can be personally identified, every disease is now a rare disease, and every treatment can now be individualized.
This poses a dilemma for those people involved in the development of new treatments. How can the safety and efficacy of a treatment be determined if the treatment is designed for a single individual? Today, safety and efficacy are determined, in large part, by randomized control trials, clinical trials that typically involve hundreds of participants. The large number of patients is required to assure statistical confidence in the outcomes of the treatments. Clearly clinical trials must be designed that are able to build assurance in the predictability of the outcomes and do this with very small test populations. The challenges are at least twofold: confidence is difficult to build with small populations, and the identification and recruitment of the trial participants is quite difficult if the number of eligible participants is very small.
The second challenge, identification of trial participants, will be the topic of future blogs and involves data mining on an extensive scale. The first challenge, building confidence once a small population has been identified, may be aided by adaptive trial-design methods. In adaptive design the rules of data collection and sampling change as data is collected. Adaptive designs allow investigators to start with a small population and change the data that is being collected as data is collected. This speeds the knowledge-accumulation process, allowing for quicker decision-making.
Adaptations to clinical trials can take several forms: adaptive randomization; stopping a trial early due to safety, futility or efficacy at interim analysis; dropping the losers (or inferior treatment groups); sample size re-estimation; modifications in inclusion/exclusion criteria; evaluability criteria, dose/regimen and treatment duration; changes in hypotheses and/or study endpoints; and modifications and/or changes made to the statistical analysis plan prior to database lock or unblinding of treatment codes. Clearly this is a major shift in process.
The FDA has encouraged this approach. The FDA released a Critical Path Opportunities List in 2006 that calls for advancing innovative trial designs, “especially for the use of prior experience or accumulated information in trial design.” According to the FDA, “the purpose of adaptive design methods in clinical trials is to give the investigator the flexibility for identifying best (optimal) clinical benefit of the test treatment under study without undermining the validity and integrity of the intended study.”
Drug pipelines have been drying up for several years. The pharmaceutical industry faced a patent cliff in 2012 in which many branded products went generic. This led to significant downsizing in the industry. Personalized medicine may be a way for the healthcare industry to revamp and deliver the next level of value to patients. Before this can happen, however, several technical challenges must be overcome. Most of these challenges require improved information management. Adaptive-clinical design may be one of the technical solutions.