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.

PARK8/LRRK2(leucine-rich repeat kinase 2) was recently identified as a causative gene for autosomal dominant Parkinson's disease (PD), withLRRK2mutation G2019S linked to the most frequent familial form of PD. Emergingin vitroevidence indicates that aberrant enzymatic activity of LRRK2 protein carrying this mutation can cause neurotoxicity. However, the physiological and pathophysiological functions of LRRK2in vivoremain elusive. Here we characterize two bacterial artificial chromosome (BAC) transgenic mouse strains overexpressing LRRK2 wild-type (Wt) or mutant G2019S. Transgenic LRRK2-Wt mice had elevated striatal dopamine (DA) release with unaltered DA uptake or tissue content. Consistent with this result, LRRK2-Wt mice were hyperactive and showed enhanced performance in motor function tests. These results suggest a role for LRRK2 in striatal DA transmission and the consequent motor function. In contrast, LRRK2-G2019S mice showed an age-dependent decrease in striatal DA content, as well as decreased striatal DA release and uptake. Despite increased brain kinase activity, LRRK2-G2019S overexpression was not associated with loss of DAergic neurons in substantia nigra or degeneration of nigrostriatal terminals at 12 months. Our results thus reveal a pivotal role for LRRK2 in regulating striatal DA transmission and consequent control of motor function. The PD-associated mutation G2019S may exert pathogenic effects by impairing these functions of LRRK2. Our LRRK2 BAC transgenic mice, therefore, could provide a useful model for understanding early PD pathological events.

Neural circuits that determine the perception and modulation of pain remain poorly understood. The prefrontal cortex (PFC) provides top-down control of sensory and affective processes. While animal and human imaging studies have shown that the PFC is involved in pain regulation, its exact role in pain states remains incompletely understood. A key output target for the PFC is the nucleus accumbens (NAc), an important component of the reward circuitry. Interestingly, recent human imaging studies suggest that the projection from the PFC to the NAc is altered in chronic pain. The function of this corticostriatal projection in pain states, however, is not known. Here we show that optogenetic activation of the PFC produces strong antinociceptive effects in a rat model (spared nerve injury model) of persistent neuropathic pain. PFC activation also reduces the affective symptoms of pain. Furthermore, we show that this pain-relieving function of the PFC is likely mediated by projections to the NAc. Thus, our results support a novel role for corticostriatal circuitry in pain regulation.

Acute pain induces depressed mood, and chronic pain is known to cause depression. Depression, meanwhile, can also adversely affect pain behaviors ranging from symptomology to treatment response. Pain and depression independently induce long-term plasticity in the central nervous system (CNS). Comorbid conditions, however, have distinct patterns of neural activation. We performed a review of the changes in neural circuitry and molecular signaling pathways that may underlie this complex relationship between pain and depression. We also discussed some of the current and future therapies that are based on this understanding of the CNS plasticity that occurs with pain and depression.

Editor’s Perspective: What We Already Know about This Topic: What This Article Tells Us That Is New: Background: Hypotension is a risk factor for adverse perioperative outcomes. Machine-learning methods allow large amounts of data for development of robust predictive analytics. The authors hypothesized that machine-learning methods can provide prediction for the risk of postinduction hypotension. Methods: Data was extracted from the electronic health record of a single quaternary care center from November 2015 to May 2016 for patients over age 12 that underwent general anesthesia, without procedure exclusions. Multiple supervised machine-learning classification techniques were attempted, with postinduction hypotension (mean arterial pressure less than 55 mmHg within 10 min of induction by any measurement) as primary outcome, and preoperative medications, medical comorbidities, induction medications, and intraoperative vital signs as features. Discrimination was assessed using cross-validated area under the receiver operating characteristic curve. The best performing model was tuned and final performance assessed using split-set validation. Results: Out of 13,323 cases, 1,185 (8.9%) experienced postinduction hypotension. Area under the receiver operating characteristic curve using logistic regression was 0.71 (95% CI, 0.70 to 0.72), support vector machines was 0.63 (95% CI, 0.58 to 0.60), naive Bayes was 0.69 (95% CI, 0.67 to 0.69), k-nearest neighbor was 0.64 (95% CI, 0.63 to 0.65), linear discriminant analysis was 0.72 (95% CI, 0.71 to 0.73), random forest was 0.74 (95% CI, 0.73 to 0.75), neural nets 0.71 (95% CI, 0.69 to 0.71), and gradient boosting machine 0.76 (95% CI, 0.75 to 0.77). Test set area for the gradient boosting machine was 0.74 (95% CI, 0.72 to 0.77). Conclusions: The success of this technique in predicting postinduction hypotension demonstrates feasibility of machine-learning models for predictive analytics in the field of anesthesiology, with performance dependent on model selection and appropriate tuning.