ABSTRACT
It is widely acknowledged that using p-value thresholds as the basis for making decision on health care spending is not appropriate. In the context of medical decision making, we argue that patient preferences need to be a stronger factor. Depending on attitudes to risk, patients might prefer a medical treatment that performs on average worse than a comparator but offers a small probability of a large gain such as a cure. However, what has been labeled 'value of hope' is not yet fully reflected in the decision-making process of drug approval and health technology assessment (HTA). Therefore, patient risk preferences should be formally incorporated within the decision-making framework for regulatory and reimbursement decisions.
ABSTRACT
AIM: The productivity of pharmaceutical research and development (R&D) investments is declining due to high failure rates in clinical research. Recently, the US Food and Drug Administration (FDA) acknowledged that adaptive designs can make drug development more efficient and less costly. Our objective is to simulate cost-saving effects and estimate the impact on global R&D expenditures as well as possible outcomes measured in life-years gained. METHODS: Based on published drug-development cost data we calculate potential cost savings derived from variations in clinical success rates that result from employing adaptive trial designs. In a subsequent step we estimate how those cost changes affect global R&D expenditures and outcomes. RESULTS: Our calculations indicate that an adaptive trial design with the potential to increase success rates of clinical trials by 4 percentage points could lower development costs for a new drug from 2.6 to 2.2bn USD. On a global scale, this cost reduction would free up an additional 4.2bn USD for investment into pharmaceutical R&D to bring about drug innovations that in turn would be capable of generating up to 3.5 million life-years. CONCLUSION: New clinical trial designs are crucial to improving productivity within the pharmaceutical industry and to fostering a sustainable health-care system.
ABSTRACT
OBJECTIVE: Glucolipotoxicity is a major pathophysiological mechanism in the development of insulin resistance and type 2 diabetes mellitus (T2D). We aimed to detect subtle changes in the circulating lipid profile by shotgun lipidomics analyses and to associate them with four different insulin sensitivity indices. METHODS: The cross-sectional study comprised 90 men with a broad range of insulin sensitivity including normal glucose tolerance (NGT, n = 33), impaired glucose tolerance (IGT, n = 32) and newly detected T2D (n = 25). Prior to oral glucose challenge plasma was obtained and quantitatively analyzed for 198 lipid molecular species from 13 different lipid classes including triacylglycerls (TAGs), phosphatidylcholine plasmalogen/ether (PC O-s), sphingomyelins (SMs), and lysophosphatidylcholines (LPCs). To identify a lipidomic signature of individual insulin sensitivity we applied three data mining approaches, namely least absolute shrinkage and selection operator (LASSO), Support Vector Regression (SVR) and Random Forests (RF) for the following insulin sensitivity indices: homeostasis model of insulin resistance (HOMA-IR), glucose insulin sensitivity index (GSI), insulin sensitivity index (ISI), and disposition index (DI). The LASSO procedure offers a high prediction accuracy and and an easier interpretability than SVR and RF. RESULTS: After LASSO selection, the plasma lipidome explained 3% (DI) to maximal 53% (HOMA-IR) variability of the sensitivity indexes. Among the lipid species with the highest positive LASSO regression coefficient were TAG 54:2 (HOMA-IR), PC O- 32:0 (GSI), and SM 40:3:1 (ISI). The highest negative regression coefficient was obtained for LPC 22:5 (HOMA-IR), TAG 51:1 (GSI), and TAG 58:6 (ISI). CONCLUSION: Although a substantial part of lipid molecular species showed a significant correlation with insulin sensitivity indices we were able to identify a limited number of lipid metabolites of particular importance based on the LASSO approach. These few selected lipids with the closest connection to sensitivity indices may help to further improve disease risk prediction and disease and therapy monitoring.
