Multilevel modeling and value of information in clinical trial decision support.

BACKGROUND: Clinical trials are the main method for evaluating safety and efficacy of medical interventions and have produced many advances in improving human health. The Women's Health Initiative overturned a half-century of harmful practice in hormone therapy, the National Lung Screening Trial identified the first successful lung cancer screening tool and the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial overturned decades-long assumptions. While some trials identify unforeseen safety issues or harms, many fail to demonstrate efficacy. Large trials require substantial resources; to ensure reliable outcomes, we must seek ways to improve the predictive information used as the basis of trials. RESULTS: Here we demonstrate a modeling framework for linking knowledge of underlying biological mechanism to evaluate the expectation of trial outcomes. Key features include the ability to propagate uncertainty in biological mechanism to uncertainty in trial outcome and mechanisms for identifying knowledge gaps most responsible for unexpected outcomes. The framework was used to model the effect of selenium supplementation for prostate cancer prevention and parallels the Selenium and Vitamin E Cancer Prevention Trial that showed no efficacy despite suggestive data from secondary endpoints in the Nutritional Prevention of Cancer trial and found increased incidence of high-grade prostate cancer in certain subgroups. CONCLUSION: Using machine learning methods, we identified the parameters of the model that are most predictive of trial outcome and found that the top four are directly related to the rates of reactions producing methylselenol and transporting extracellular selenium into the cell as selenide. This modeling process demonstrates how the approach can be used in advance of a large clinical trial to identify the best targets for conducting further research to reduce the uncertainty in the trial outcome.

Examining the origins of the hydration force between lipid bilayers using all-atom simulations.

Using 237 all-atom double bilayer simulations, we examined the thermodynamic and structural changes that occur as a phosphatidylcholine lipid bilayer stack is dehydrated. The simulated system represents a micropatch of lipid multilayer systems that are studied experimentally using surface force apparatus, atomic force microscopy and osmotic pressure studies. In these experiments, the hydration level of the system is varied, changing the separation between the bilayers, in order to understand the forces that the bilayers feel as they are brought together. These studies have found a curious, strongly repulsive force when the bilayers are very close to each other, which has been termed the "hydration force," though the origins of this force are not clearly understood. We computationally reproduce this repulsive, relatively free energy change as bilayers come together and make qualitative conclusions as to the enthalpic and entropic origins of the free energy change. This analysis is supported by data showing structural changes in the waters, lipids and salts that have also been seen in experimental work. Increases in solvent ordering as the bilayers are dehydrated are found to be essential in causing the repulsion as the bilayers come together.