Enhancing Natural Product Clinical Trials (P13-037-19). (13th June 2019) - Sorkin, Barbara; Kuszak, Adam; Pauli, Guido; Bloss, Gregory; Barrett, Bruce; Ferruzzi, Mario; Fukagawa, Naomi; Kiely, Mairead; Lakens, Daniel; Meltzer, David; Paul, Jeffrey; Sipes, Nisha
Abstract: Objectives: To discuss good practices and criteria for optimal design and interpretation of pre-clinical and clinical natural product (NP) research in order to increase benefit from our investment in NP clinical trials (CT). Background: Large, randomized, controlled CT often fail to reject the null hypothesis or show rigorous evidence of benefit. This includes recent large, NIH-supported CT of nutrients such as vitamin D and selenium, and of botanical dietary supplements. Negative and positive outcomes may be equally important for public health, but because large CT cost at least $20 M each, plus opportunity costs, it is important that CT designs maximize the yield of actionable information regardless of outcome. Methods: Experts and stakeholders from academia, government and the private sector collaboratively developed a broadly attended workshop in which good practices to enhance rigor, reproducibility and translational relevance were discussed. Results: N/A. Conclusions: Critical issues in CT design include product identity, reproducibility and pharmacology (where feasible), power to test a primary outcome significant to consumers, and placebo controls. When basing a CT on traditional use or prior in vitro or in vivo studies, similarity of product (e.g., source identity, methods of preparation, form and intake), health outcome and population (e.g., age, sex, genetics, diet and environment), require careful consideration. Appropriate controls for known types of in vitro assay interference (e.g., aggregation, membrane disruption, protein denaturation) are imperative. Compounds with limited bioavailability, or activity only at concentrations above those achievable by ingestion, are likely poor candidates for dietary CT. Translational validity of model systems should be carefully assessed. Appropriate analyses (e.g., p -curve and meta-regression methods) should be used to obtain bias-corrected effect size estimates, and to identify research areas where the evidence base may be weaker than published findings suggest. Finally, CT prioritization should consider expected impact on public health, and whether known NP causal mechanisms of action are such that useful information, e.g., on product bioavailability or biological activity, are generated even if the completed CT fails to reject the null hypothesis. Funding Sources: NIH, FDA, USDA.