Pain medication plays an important role in the treatment of acute and chronic pain conditions, but some drugs, opioids in particular, have been overprescribed or prescribed without adequate safeguards, leading to an alarming rise in medication-related overdose deaths. The NIH Helping to End Addiction Long-term (HEAL) Initiative is a trans-agency effort to provide scientific solutions to stem the opioid crisis. One component of the initiative is to support biomarker discovery and rigorous validation in collaboration with industry leaders to accelerate high-quality clinical research into neurotherapeutics and pain. The use of objective biomarkers and clinical trial end points throughout the drug discovery and development process is crucial to help define pathophysiological subsets of pain, evaluate target engagement of new drugs and predict the analgesic efficacy of new drugs. In 2018, the NIH-led Discovery and Validation of Biomarkers to Develop Non-Addictive Therapeutics for Pain workshop convened scientific leaders from academia, industry, government and patient advocacy groups to discuss progress, challenges, gaps and ideas to facilitate the development of biomarkers and end points for pain. The outcomes of this workshop are outlined in this Consensus Statement.

Often repeated measures data are summarized into pre-post-treatment measurements. Various methods exist in the literature for estimating and testing treatment effect, including ANOVA, analysis of covariance (ANCOVA), and linear mixed modeling (LMM). Under the first two methods, outcomes can either be modeled as the post treatment measurement (ANOVA-POST or ANCOVA-POST), or a change score between pre and post measurements (ANOVA-CHANGE, ANCOVA-CHANGE). In LMM, the outcome is modeled as a vector of responses with or without Kenward-Rogers adjustment. We consider five methods common in the literature, and discuss them in terms of supporting simulations and theoretical derivations of variance. Consistent with existing literature, our results demonstrate that each method leads to unbiased treatment effect estimates, and based on precision of estimates, 95% coverage probability, and power, ANCOVA modeling of either change scores or post-treatment score as the outcome, prove to be the most effective. We further demonstrate each method in terms of a real data example to exemplify comparisons in real clinical context.

This paper describes a sensitive, specific and rapid high-performance liquid chromatography (HPLC) method for the determination of curcumin in rat plasma. After a simple step of protein precipitation in 96-well format using acetonitrile containing the internal standard (IS), emodin, plasma samples were analyzed by reverse-phase HPLC. Curcumin and the IS emodin were separated on a Diamonsil C(18) analytical column (4.6 x 100 mm, 5 microm) using acetonitrile-5% acetic acid (75:25, v/v) as mobile phase at a flow rate of 1.0 mL/min. The method was sensitive with a lower limit of quantitation of 1 ng/mL, with good linearity (r(2) >or= 0.999) over the linear range 1-500 ng/mL. All the validation data, such as accuracy and precision, were within the required limits. A run time of 3.0 min for each sample made high-throughput bioanalysis possible. The assay method was successfully applied to the study of the pharmacokinetics of curcumin liposome in rats.