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Objective: Pain assessment in children continues to challenge clinicians and researchers, as subjective experiences of pain require inference through observable behaviors, both involuntary and deliberate. The presented approach supplements the subjective self-report-based method by fusing electrodermal activity (EDA) recordings with video facial expressions to develop an objective pain assessment metric. Such an approach is specifically important for assessing pain in children who are not capable of providing accurate self-pain reports, requiring nonverbal pain assessment. We demonstrate the performance of our approach using data recorded from children in post-operative recovery following laparoscopic appendectomy. We examined separately and combined the usefulness of EDA and video facial expression data as predictors of children's self-reports of pain following surgery through recovery. Findings indicate that EDA and facial expression data independently provide above chance sensitivities and specificities, but their fusion for classifying clinically significant pain vs. clinically nonsignificant pain achieved substantial improvement, yielding 90.91% accuracy, with 100% sensitivity and 81.82% specificity. The multimodal measures capitalize upon different features of the complex pain response. Thus, this paper presents both evidence for the utility of a weighted maximum likelihood algorithm as a novel feature selection method for EDA and video facial expression data and an accurate and objective automated classification algorithm capable ofdiscriminating clinically significant pain from clinically nonsignificant pain in children. [ABSTRACT FROM AUTHOR]

The current study examined youths' and their parents' perceptions concerning participation in an investigation of spontaneous and induced pain during recovery from laparoscopic appendectomy. Youth (age range = 5–17 years) and their parents independently completed surveys about their study participation. On a scale from 0 (very negative) to 10 (very positive), both parents (M = 9.4, SD = 1.3) and youth (M = 7.9, SD = 2.4) rated their experience as positive. Among youth, experience ratings did not differ by pain severity and survey responses did not differ by age. Most youth (83%) reported that they would tell another youth to participate. Ethical issues regarding instigation of pain in youth for research purposes are examined. [ABSTRACT FROM AUTHOR]

Despite recent advances in asthma management with anti-IL-5 therapies, many patients have eosinophilic asthma that remains poorly controlled. IL-3 shares a common β subunit receptor with both IL-5 and GM-CSF but, through α-subunit-specific properties, uniquely influences eosinophil biology and may serve as a potential therapeutic target. We aimed to globally characterize the transcriptomic profiles of GM-CSF, IL-3, and IL-5 stimulation on human circulating eosinophils and identify differences in gene expression using advanced statistical modeling. Human eosinophils were isolated from the peripheral blood of healthy volunteers and stimulated with either GM-CSF, IL-3, or IL-5 for 48 h. RNA was then extracted and bulk sequencing performed. DESeq analysis identified differentially expressed genes and weighted gene coexpression network analysis independently defined modules of genes that are highly coexpressed. GM-CSF, IL-3, and IL-5 commonly upregulated 252 genes and downregulated 553 genes, producing a proinflammatory and survival phenotype that was predominantly mediated through TWEAK signaling. IL-3 stimulation yielded the most numbers of differentially expressed genes that were also highly coexpressed (n = 119). These genes were enriched in pathways involving JAK/STAT signaling. GM-CSF and IL-5 stimulation demonstrated redundancy in eosinophil gene expression. In conclusion, IL-3 produces a distinct eosinophil gene expression program among the β-chain receptor cytokines. IL-3-upregulated genes may provide a foundation for research into therapeutics for patients with eosinophilic asthma who do not respond to anti-IL-5 therapies. [ABSTRACT FROM AUTHOR]

Objective pain assessment is required for appropriate pain management in the clinical setting. However, clinical gold standard pain assessment is based on subjective methods. Automated pain detection from physiological data may provide important objective information to better standardize pain assessment. Specifically, electrodermal activity (EDA) can identify features of stress and anxiety induced by varying pain levels. However, notable variability in EDA measurement exists and research to date has demonstrated sensitivity but lack of specificity in pain assessment. In this paper, we use timescale decomposition (TSD) to extract salient features from EDA signals to identify an accurate and automated EDA pain detection algorithm to sensitively and specifically distinguish pain from no-pain conditions.

Accurately determining pain levels in children is difficult, even for trained professionals and parents. Facial activity provides sensitive and specific information about pain, and computer vision algorithms have been developed to automatically detect Facial Action Units (AUs) defined by the Facial Action Coding System (FACS). Our prior work utilized information from computer vision, i.e., automatically detected facial AUs, to develop classifiers to distinguish between pain and no-pain conditions. However, application of pain/no-pain classifiers based on automated AU codings across different environmental domains results in diminished performance. In contrast, classifiers based on manually coded AUs demonstrate reduced environmentally-based variability in performance. In this paper, we train a machine learning model to recognize pain using AUs coded by a computer vision system embedded in a software package called iMotions. We also study the relationship between iMotions (automatically) and human (manually) coded AUs. We find that AUs coded automatically are different from those coded by a human trained in the FACS system, and that the human coder is less sensitive to environmental changes. To improve classification performance in the current work, we applied transfer learning by training another machine learning model to map automated AU codings to a subspace of manual AU codings to enable more robust pain recognition performance when only automatically coded AUs are available for the test data. With this transfer learning method, we improved the Area Under the ROC Curve (AUC) on independent data from new participants in our target domain from 0.67 to 0.72.

Accurately determining pain levels in children is difficult, even for trained professionals and parents. Facial activity and electro- dermal activity (EDA) provide rich information about pain, and both have been used in automated pain detection. In this paper, we discuss preliminary steps towards fusing models trained on video and EDA features respectively. We compare fusion models using original video features and those using transferred video features which are less sensitive to environmental changes. We demonstrate the benefit of the fusion and the transferred video features with a special test case involving domain adaptation and improved performance relative to using EDA and video features alone.

Background: Current pain assessment methods in youth are suboptimal and vulnerable to bias and underrecognition of clinical pain. Facial expressions are a sensitive, specific biomarker of the presence and severity of pain, and computer vision (CV) and machine-learning (ML) techniques enable reliable, valid measurement of pain-related facial expressions from video. We developed and evaluated a CVML approach to measure pain-related facial expressions for automated pain assessment in youth., Methods: A CVML-based model for assessment of pediatric postoperative pain was developed from videos of 50 neurotypical youth 5 to 18 years old in both endogenous/ongoing and exogenous/transient pain conditions after laparoscopic appendectomy. Model accuracy was assessed for self-reported pain ratings in children and time since surgery, and compared with by-proxy parent and nurse estimates of observed pain in youth., Results: Model detection of pain versus no-pain demonstrated good-to-excellent accuracy (Area under the receiver operating characteristic curve 0.84-0.94) in both ongoing and transient pain conditions. Model detection of pain severity demonstrated moderate-to-strong correlations (r = 0.65-0.86 within; r = 0.47-0.61 across subjects) for both pain conditions. The model performed equivalently to nurses but not as well as parents in detecting pain versus no-pain conditions, but performed equivalently to parents in estimating pain severity. Nurses were more likely than the model to underestimate youth self-reported pain ratings. Demographic factors did not affect model performance., Conclusions: CVML pain assessment models derived from automatic facial expression measurements demonstrated good-to-excellent accuracy in binary pain classifications, strong correlations with patient self-reported pain ratings, and parent-equivalent estimation of children's pain levels over typical pain trajectories in youth after appendectomy., (Copyright © 2015 by the American Academy of Pediatrics.)

Sodium-glucose cotransporter 2 (SGLT2) inhibitors are a new class of oral anti-diabetic agents that improve glycemic control by inhibiting SGLT2-mediated renal glucose reabsorption. Currently available agents increase urinary glucose excretion (UGE) to <50% of maximal values because they do not inhibit SGLT1, which reabsorbs >50% of filtered glucose when SGLT2 is completely inhibited. This led us to test whether LP-925219, a small molecule dual SGLT1/SGLT2 inhibitor, increases UGE to maximal values in wild-type (WT) mice. We first tested LP-925219 inhibition of glucose transport by HEK293 cells expressing SGLT1 or SGLT2, and then characterized LP-925219 pharmacokinetics. We found that LP-925219 was a potent inhibitor of mouse SGLT1 (IC50 = 22.6 nmol/L) and SGLT2 (IC50 = 0.5 nmol/L), and that a 10 mg/kg oral dose was bioavailable (87%) with a long half-life (7 h). We next delivered LP-925219 by oral gavage to WT, SGLT1 knockout (KO), SGLT2 KO, and SGLT1/SGLT2 double KO (DKO) mice and measured their 24-h UGE. We found that, in vehicle-treated mice, DKO UGE was maximal and SGLT2 KO, SGLT1 KO, and WT UGEs were 30%, 2%, and 0.2% of maximal, respectively; we also found that LP-925219 dosed at 60 mg/kg twice daily increased UGE of SGLT1 KO, SGLT2 KO, and WT mice to DKO UGE levels. These findings show that orally available dual SGLT1/SGLT2 inhibitors can maximize 24-h UGE in mammals, and suggest that such agents merit further evaluation for their potential, in diabetic patients, to achieve better glycemic control than is achieved using selective SGLT2 inhibitors.

Phenoxypropoxybiguanides, such as PS-15, are antimalarial prodrugs analogous to the relationship of proguanil and its active metabolite cycloguanil. Unlike cycloguanil, however, WR99210, the active metabolite of PS-15, has retained in vitro potency against newly emerging antifolate-resistant malaria parasites. Recently, in vitro metabolism of a new series of phenoxypropoxybiguanide analogs has examined the production of the active triazine metabolites by human liver microsomes. The purpose of this investigation was to elucidate the primary cytochrome P450 isoforms involved in the production of active metabolites in the current lead candidate. By using expressed human recombinant isoform preparations, specific chemical inhibitors, and isoform-specific inhibitory antibodies, the primary cytochrome P450 isoforms involved in the in vitro metabolic activation of JPC-2056 were elucidated. Unlike proguanil, which is metabolized primarily by CYP2C19, the results indicate that CYP3A4 plays a more important role in the metabolism of both PS-15 and JPC-2056. Whereas CYP2D6 appears to play a major role in the metabolism of PS-15 to WR99210, it appears less important in the conversion of JPC-2056 to JPC-2067. These results are encouraging, considering the prominence of CYP2C19 and CYP2D6 polymorphisms in certain populations at risk for contracting malaria, because the current clinical prodrug candidate from this series may be less dependent on these enzymes for metabolic activation.

Mixed tetraoxanes 5a and 13 synthesized from cholic acid and 4-oxocyclohexanecarboxylic acid were as active as artemisinin against chloroquine-susceptible, chloroquine-resistant, and multidrug-resistant Plasmodium falciparum strains (IC50, IC90). Most active 13 is metabolically stable in in vitro metabolism studies. In vivo studies on tetraoxanes with a C(4' ') methyl group afforded compound 15, which cured 4/5 mice at 600 and 200 mg.kg-1.day-1, and 2/5 mice at 50 mg.kg-1.day-1, showing no toxic effects. Tetraoxane 19 was an extremely active antiproliferative with LC50 of 17 nM and maximum tolerated dose of 400 mg/kg. In Fe(II)-induced scission of tetraoxane antimalarials only RO* radicals were detected by EPR experiments. This finding and the indication of Fe(IV)=O species led us to propose that RO* radicals are probably capable of inducing the parasite's death. Our results suggest that C radicals are possibly not the only lethal species derived from peroxide prodrug antimalarials, as currently believed.

Phenoxypropoxybiguanides, such as 1 (PS-15), are prodrugs analogous to the relationship of proguanil and its active metabolite cycloguanil. Unlike cycloguanil, however, 1a (WR99210), the active metabolite of 1, has retained in vitro potency against newly emerging antifolate-resistant malaria parasites. Unfortunately, manufacturing processes and gastrointestinal intolerance have prevented the clinical development of 1. In vitro antimalarial activity and in vitro metabolism studies have been performed on newly synthesized phenoxypropoxybiguanide analogues. All of the active dihydrotriazine metabolites exhibited potent antimalarial activity with in vitro IC(50) values less than 0.04 ng/mL. In vitro metabolism studies in human liver microsomes identified the production of not only the active dihydrotriazine metabolite, but also a desalkylation on the carbonyl chain, and multiple hydroxylated metabolites. The V(max) for production of the active metabolites ranged from 10.8 to 27.7 pmol/min/mg protein with the K(m) ranging from 44.8 to 221 microM. The results of these studies will be used to guide the selection of a lead candidate.

The high level of attrition of drugs in clinical development has led pharmaceutical companies to increase the efficiency of their lead identification and development through techniques such as combinatorial chemistry and high-throughput (HTP) screening. Since the major reasons for clinical drug candidate failure other than efficacy are pharmacokinetics and toxicity, attention has been focused on assessing properties such as metabolic stability, drug-drug interactions (DDI), and absorption earlier in the drug discovery process. Animal studies are simply too labor-intensive and expensive to use for evaluating every hit, so it has been necessary to develop and implement higher throughput in vitro ADME screens to manage the large number of compounds of interest. The antimalarial drug development program at the Walter Reed Army Institute of Research, Division of Experimental Therapeutics (WRAIR/ET) has adopted this paradigm in its search for a long-term prophylactic for the prevention of malaria. The overarching goal of this program is to develop new, long half-life, orally bioavailable compounds with potent intrinsic activity against liver- and blood-stage parasites. From the WRAIR HTP antimalarial screen, numerous compounds are regularly identified with potent activity. These hits are now immediately evaluated using a panel of in vitro ADME screens to identify and predict compounds that will meet our specific treatment criteria. In this review, the WRAIR ADME screening program for antimalarial drugs is described as well as how we have implemented it to predict the ADME properties of small molecule for the identification of promising drug candidates.