Topically Applied Therapies for the Treatment of Skin Disease: Past, Present, and Future.

The purpose of this review is to summarize essential biological, pharmaceutical, and clinical aspects in the field of topically applied medicines that may help scientists when trying to develop new topical medicines. After a brief history of topical drug delivery, a review of the structure and function of the skin and routes of drug absorption and their limitations is provided. The most prevalent diseases and current topical treatment approaches are then detailed, the organization of which reflects the key disease categories of autoimmune and inflammatory diseases, microbial infections, skin cancers, and genetic skin diseases. The complexity of topical product development through to large-scale manufacturing along with recommended risk mitigation approaches are then highlighted. As such topical treatments are applied externally, patient preferences along with the challenges they invoke are then described, and finally the future of this field of drug delivery is discussed, with an emphasis on areas that are more likely to yield significant improvements over the topical medicines in current use or would expand the range of medicines and diseases treatable by this route of administration. SIGNIFICANCE STATEMENT: This review of the key aspects of the skin and its associated diseases and current treatments along with the intricacies of topical formulation development should be helpful in making judicious decisions about the development of new or improved topical medicines. These aspects include the choices of the active ingredients, formulations, the target patient population's preferences, limitations, and the future with regard to new skin diseases and topical medicine approaches.

Offline Two-Dimensional Liquid Chromatography-Mass Spectrometry for Deep Annotation of the Fecal Metabolome Following Fecal Microbiota Transplantation.

Biological interpretation of untargeted LC-MS-based metabolomics data depends on accurate compound identification, but current techniques fall short of identifying most features that can be detected. The human fecal metabolome is complex, variable, incompletely annotated, and serves as an ideal matrix to evaluate novel compound identification methods. We devised an experimental strategy for compound annotation using multidimensional chromatography and semiautomated feature alignment and applied these methods to study the fecal metabolome in the context of fecal microbiota transplantation (FMT) for recurrent C. difficile infection. Pooled fecal samples were fractionated using semipreparative liquid chromatography and analyzed by an orthogonal LC-MS/MS method. The resulting spectra were searched against commercial, public, and local spectral libraries, and annotations were vetted using retention time alignment and prediction. Multidimensional chromatography yielded more than a 2-fold improvement in identified compounds compared to conventional LC-MS/MS and successfully identified several rare and previously unreported compounds, including novel fatty-acid conjugated bile acid species. Using an automated software-based feature alignment strategy, most metabolites identified by the new approach could be matched to features that were detected but not identified in single-dimensional LC-MS/MS data. Overall, our approach represents a powerful strategy to enhance compound identification and biological insight from untargeted metabolomics data.

Nutrient-dependent regulation of ß-cell proinsulin content.

Insulin is made from proinsulin, but the extent to which fasting/feeding controls the homeostatically regulated proinsulin pool in pancreatic ß-cells remains largely unknown. Here, we first examined ß-cell lines (INS1E and Min6, which proliferate slowly and are routinely fed fresh medium every 2-3 days) and found that the proinsulin pool size responds to each feeding within 1 to 2 h, affected both by the quantity of fresh nutrients and the frequency with which they are provided. We observed no effect of nutrient feeding on the overall rate of proinsulin turnover as quantified from cycloheximide-chase experiments. We show that nutrient feeding is primarily linked to rapid dephosphorylation of translation initiation factor eIF2α, presaging increased proinsulin levels (and thereafter, insulin levels), followed by its rephosphorylation during the ensuing hours that correspond to a fall in proinsulin levels. The decline of proinsulin levels is blunted by the integrated stress response inhibitor, ISRIB, or by inhibition of eIF2α rephosphorylation with a general control nonderepressible 2 (not PERK) kinase inhibitor. In addition, we demonstrate that amino acids contribute importantly to the proinsulin pool; mass spectrometry shows that ß-cells avidly consume extracellular glutamine, serine, and cysteine. Finally, we show that in both rodent and human pancreatic islets, fresh nutrient availability dynamically increases preproinsulin, which can be quantified without pulse-labeling. Thus, the proinsulin available for insulin biosynthesis is rhythmically controlled by fasting/feeding cycles.

In-Depth Chemical Analysis of the Brain Extracellular Space Using In Vivo Microdialysis with Liquid Chromatography-Tandem Mass Spectrometry.

Metabolomic analysis of samples acquired in vivo from the brain extracellular space by microdialysis sampling can provide insights into chemical underpinnings of a given brain state and how it changes over time. Small sample volumes and low physiological concentrations have limited the identification of compounds from this compartment, so at present, we have scant knowledge of its composition. As a result, most in vivo measurements have limited depth of analysis. Here, we describe an approach to (1) identify hundreds of compounds in brain dialysate and (2) routinely detect many of these compounds in 5 µL microdialysis samples to enable deep monitoring of brain chemistry in time-resolved studies. Dialysate samples collected over 12 h were concentrated 10-fold and then analyzed using liquid chromatography with iterative tandem mass spectrometry (LC-MS/MS). Using this approach on dialysate from the rat striatum with both reversed-phase and hydrophilic interaction liquid chromatography yielded 479 unique compound identifications. 60% of the identified compounds could be detected in 5 µL of dialysate without further concentration using a single 20 min LC-MS analysis, showing that once identified, most compounds can be detected using small sample volumes and shorter analysis times compatible with routine in vivo monitoring. To detect more neurochemicals, LC-MS analysis of dialysate derivatized with light and isotopically labeled benzoyl chloride was employed. 872 nondegenerate benzoylated features were detected with this approach, including most small-molecule neurotransmitters and several metabolites involved in dopamine metabolism. This strategy allows deeper annotation of the brain extracellular space than previously possible and provides a launching point for defining the chemistry underlying brain states.

The molecular and metabolic program by which white adipocytes adapt to cool physiologic temperatures.

Although visceral adipocytes located within the body's central core are maintained at approximately 37°C, adipocytes within bone marrow, subcutaneous, and dermal depots are found primarily within the peripheral shell and generally exist at cooler temperatures. Responses of brown and beige/brite adipocytes to cold stress are well studied; however, comparatively little is known about mechanisms by which white adipocytes adapt to temperatures below 37°C. Here, we report that adaptation of cultured adipocytes to 31°C, the temperature at which distal marrow adipose tissues and subcutaneous adipose tissues often reside, increases anabolic and catabolic lipid metabolism, and elevates oxygen consumption. Cool adipocytes rely less on glucose and more on pyruvate, glutamine, and, especially, fatty acids as energy sources. Exposure of cultured adipocytes and gluteal white adipose tissue (WAT) to cool temperatures activates a shared program of gene expression. Cool temperatures induce stearoyl-CoA desaturase-1 (SCD1) expression and monounsaturated lipid levels in cultured adipocytes and distal bone marrow adipose tissues (BMATs), and SCD1 activity is required for acquisition of maximal oxygen consumption at 31°C.

Compound Identification Strategies in Mass Spectrometry-Based Metabolomics and Pharmacometabolomics.

The metabolome is composed of a vast array of molecules, including endogenous metabolites and lipids, diet- and microbiome-derived substances, pharmaceuticals and supplements, and exposome chemicals. Correct identification of compounds from this diversity of classes is essential to derive biologically relevant insights from metabolomics data. In this chapter, we aim to provide a practical overview of compound identification strategies for mass spectrometry-based metabolomics, with a particular eye toward pharmacologically-relevant studies. First, we describe routine compound identification strategies applicable to targeted metabolomics. Next, we discuss both experimental (data acquisition-focused) and computational (software-focused) strategies used to identify unknown compounds in untargeted metabolomics data. We then discuss the importance of, and methods for, assessing and reporting the level of confidence of compound identifications. Throughout the chapter, we discuss how these steps can be implemented using today's technology, but also highlight research underway to further improve accuracy and certainty of compound identification. For readers interested in interpreting metabolomics data already collected, this chapter will supply important context regarding the origin of the metabolite names assigned to features in the data and help them assess the certainty of the identifications. For those planning new data acquisition, the chapter supplies guidance for designing experiments and selecting analysis methods to enable accurate compound identification, and it will point the reader toward best-practice data analysis and reporting strategies to allow sound biological and pharmacological interpretation.

Modifying Chromatography Conditions for Improved Unknown Feature Identification in Untargeted Metabolomics.

Untargeted metabolomics is an essential component of systems biology research, but it is plagued by a high proportion of detectable features not identified with a chemical structure. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments produce spectra that can be searched against databases to help identify or classify these unknowns, but many features do not generate spectra of sufficient quality to enable successful annotation. Here, we explore alterations to gradient length, mass loading, and rolling precursor ion exclusion parameters for reversed phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC) that improve compound identification performance for human plasma samples. A manual review of spectral matches from the HILIC data set was used to determine reasonable thresholds for search score and other metrics to enable semi-automated MS/MS data analysis. Compared to typical LC-MS/MS conditions, methods adapted for compound identification increased the total number of unique metabolites that could be matched to a spectral database from 214 to 2052. Following data alignment, 68.0% of newly identified features from the modified conditions could be detected and quantitated using a routine 20-min LC-MS run. Finally, a localized machine learning model was developed to classify the remaining unknowns and select a subset that shared spectral characteristics with successfully identified features. A total of 576 and 749 unidentified features in the HILIC and RPLC data sets were classified by the model as high-priority unknowns or higher-importance targets for follow-up analysis. Overall, our study presents a simple strategy to more deeply annotate untargeted metabolomics data for a modest additional investment of time and sample.

metabCombiner: Paired Untargeted LC-HRMS Metabolomics Feature Matching and Concatenation of Disparately Acquired Data Sets.

LC-HRMS experiments detect thousands of compounds, with only a small fraction of them identified in most studies. Traditional data processing pipelines contain an alignment step to assemble the measurements of overlapping features across samples into a unified table. However, data sets acquired under nonidentical conditions are not amenable to this process, mostly due to significant alterations in chromatographic retention times. Alignment of features between disparately acquired LC-MS metabolomics data could aid collaborative compound identification efforts and enable meta-analyses of expanded data sets. Here, we describe metabCombiner, a new computational pipeline for matching known and unknown features in a pair of untargeted LC-MS data sets and concatenating their abundances into a combined table of intersecting feature measurements. metabCombiner groups features by mass-to-charge (m/z) values to generate a search space of possible feature pair alignments, fits a spline through a set of selected retention time ordered pairs, and ranks alignments by m/z, mapped retention time, and relative abundance similarity. We evaluated this workflow on a pair of plasma metabolomics data sets acquired with different gradient elution methods, achieving a mean absolute retention time prediction error of roughly 0.06 min and a weighted per-compound matching accuracy of approximately 90%. We further demonstrate the utility of this method by comprehensively mapping features in urine and muscle metabolomics data sets acquired from different laboratories. metabCombiner has the potential to bridge the gap between otherwise incompatible metabolomics data sets and is available as an R package at https://github.com/hhabra/metabCombiner and Bioconductor.

Computed tomography angiography as an adjunct to computational fluid dynamics for prediction of oxygenator thrombus formation.

INTRODUCTION: Extracorporeal membrane oxygenation circuit performance can be compromised by oxygenator thrombosis. Stagnant blood flow in the oxygenator can increase the risk of thrombus formation. To minimize thrombogenic potential, computational fluid dynamics is frequently applied for identification of stagnant flow conditions. We investigate the use of computed tomography angiography to identify flow patterns associated with thrombus formation. METHODS: A computed tomography angiography was performed on a Quadrox D oxygenator, and video densitometric parameters associated with flow stagnation were measured from the acquired videos. Computational fluid dynamics analysis of the same oxygenator was performed to establish computational fluid dynamics-based flow characteristics. Forty-one Quadrox D oxygenators were sectioned following completion of clinical use. Section images were analyzed with software to determine oxygenator clot burden. Linear regression was used to correlate clot burden to computed tomography angiography and computational fluid dynamics-based flow characteristics. RESULTS: Clot burden from the explanted oxygenators demonstrated a well-defined pattern, with the largest clot burden at the corner opposite the blood inlet and outlet. The regression model predicted clot burden by region of interest as a function of time to first opacification on computed tomography angiography (R2 = 0.55). The explanted oxygenator clot burden map agreed well with the computed tomography angiography predicted clot burden map. The computational fluid dynamics parameter of residence time, when summed in the Z-direction, was partially predictive of clot burden (R2 = 0.35). CONCLUSION: In the studied oxygenator, clot burden follows a pattern consistent with clinical observations. Computed tomography angiography-based flow analysis provides a useful adjunct to computational fluid dynamics-based flow analysis in understanding oxygenator thrombus formation.

Metabolomic profiles associated with subtypes of prediabetes among Mexican Americans in Starr County, Texas, USA.

AIMS/HYPOTHESIS: To understand the complex metabolic changes that occur long before the diagnosis of type 2 diabetes, we investigated differences in metabolomic profiles in plasma between prediabetic and normoglycaemic individuals for subtypes of prediabetes defined by fasting glucose, 2 h glucose and HbA1c measures. METHODS: Untargeted metabolomics data were obtained from 155 plasma samples from 127 Mexican American individuals from Starr County, TX, USA. None had type 2 diabetes at the time of sample collection and 69 had prediabetes by at least one criterion. We tested statistical associations of amino acids and other metabolites with each subtype of prediabetes. RESULTS: We identified distinctive differences in amino acid profiles between prediabetic and normoglycaemic individuals, with further differences in amino acid levels among subtypes of prediabetes. When testing all named metabolites, several fatty acids were also significantly associated with 2 h glucose levels. Multivariate discriminative analyses show that untargeted metabolomic data have considerable potential for identifying metabolic differences among subtypes of prediabetes. CONCLUSIONS/INTERPRETATION: People with each subtype of prediabetes have a distinctive metabolomic signature, beyond the well-known differences in branched-chain amino acids. DATA AVAILABILITY: Metabolomics data are available through the NCBI database of Genotypes and Phenotypes (dbGaP, accession number phs001166; www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001166.v1.p1).

Correction to: ACE2 activation protects against cognitive decline and reduces amyloid pathology in the Tg2576 mouse model of Alzheimer's disease.

Unfortunately, the acknowledgement section was not included in the original publication.

ACE2 activation protects against cognitive decline and reduces amyloid pathology in the Tg2576 mouse model of Alzheimer's disease.

Mid-life hypertension and cerebrovascular dysfunction are associated with increased risk of later life dementia, including Alzheimer's disease (AD). The classical renin-angiotensin system (cRAS), a physiological regulator of blood pressure, functions independently within the brain and is overactive in AD. cRAS-targeting anti-hypertensive drugs are associated with reduced incidence of AD, delayed onset of cognitive decline, and reduced levels of Aß and tau in both animal models and human pathological studies. cRAS activity is moderated by a downstream regulatory RAS pathway (rRAS), which is underactive in AD and is strongly associated with pathological hallmarks in human AD, and cognitive decline in animal models of CNS disease. We now show that enhancement of brain ACE2 activity, a major effector of rRAS, by intraperitoneal administration of diminazene aceturate (DIZE), an established activator of ACE2, lowered hippocampal Aß and restored cognition in mid-aged (13-14-month-old) symptomatic Tg2576 mice. We confirmed that the protective effects of DIZE were directly mediated through ACE2 and were associated with reduced hippocampal soluble Aß42 and IL1-ß levels. DIZE restored hippocampal MasR levels in conjunction with increased NMDA NR2B and downstream ERK signalling expression in hippocampal synaptosomes from Tg2576 mice. Chronic (10 weeks) administration of DIZE to pre-symptomatic 9-10-month-old Tg2576 mice, and acute (10 days) treatment in cognitively impaired 12-13-month-old mice, prevented the development of cognitive impairment. Together these data demonstrate that ACE2 enhancement protects against and reverses amyloid-related hippocampal pathology and cognitive impairment in a preclinical model of AD.

Untargeted Metabolomics Differentiates l-Carnitine Treated Septic Shock 1-Year Survivors and Nonsurvivors.

l-Carnitine is a candidate therapeutic for the treatment of septic shock, a condition that carries a ≥40% mortality. Responsiveness to l-carnitine may hinge on unique metabolic profiles that are not evident from the clinical phenotype. To define these profiles, we performed an untargeted metabolomic analysis of serum from 21 male sepsis patients enrolled in a placebo-controlled l-carnitine clinical trial. Although treatment with l-carnitine is known to induce changes in the sepsis metabolome, we found a distinct set of metabolites that differentiated 1-year survivors from nonsurvivors. Following feature alignment, we employed a new and innovative data reduction strategy followed by false discovery correction, and identified 63 metabolites that differentiated carnitine-treated 1-year survivors versus nonsurvivors. Following identification by MS/MS and database search, several metabolite markers of vascular inflammation were determined to be prominently elevated in the carnitine-treated nonsurvivor cohort, including fibrinopeptide A, allysine, and histamine. While preliminary, these results corroborate that metabolic profiles may be useful to differentiate l-carnitine treatment responsiveness. Furthermore, these data show that the metabolic signature of l-carnitine-treated nonsurvivors is associated with a severity of illness (e.g., vascular inflammation) that is not routinely clinically detected.

Dimethyl Itaconate Is Not Metabolized into Itaconate Intracellularly.

Itaconic acid is an important metabolite produced by macrophages after stimulation with LPS. The role of itaconate in the inflammatory cascade is unclear. Here we used [13C]itaconate and dimethyl [13C]itaconate (DMI) to probe itaconate metabolism, and find that [13C]DMI is not metabolized to itaconate. [13C]Itaconate in the cell culture medium leads to elevated intracellular levels of unlabeled succinate, with no evidence of intracellular uptake. The goal of this study is to encourage the development of effective pro-drug strategies to increase the intracellular levels of itaconate, which will enable more conclusive analysis of its action on macrophages and other cell and tissue types.

Sparse network modeling and metscape-based visualization methods for the analysis of large-scale metabolomics data.

MOTIVATION: Recent technological advances in mass spectrometry, development of richer mass spectral libraries and data processing tools have enabled large scale metabolic profiling. Biological interpretation of metabolomics studies heavily relies on knowledge-based tools that contain information about metabolic pathways. Incomplete coverage of different areas of metabolism and lack of information about non-canonical connections between metabolites limits the scope of applications of such tools. Furthermore, the presence of a large number of unknown features, which cannot be readily identified, but nonetheless can represent bona fide compounds, also considerably complicates biological interpretation of the data. RESULTS: Leveraging recent developments in the statistical analysis of high-dimensional data, we developed a new Debiased Sparse Partial Correlation algorithm (DSPC) for estimating partial correlation networks and implemented it as a Java-based CorrelationCalculator program. We also introduce a new version of our previously developed tool Metscape that enables building and visualization of correlation networks. We demonstrate the utility of these tools by constructing biologically relevant networks and in aiding identification of unknown compounds. AVAILABILITY AND IMPLEMENTATION: http://metscape.med.umich.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A rapidly acquired foraging-based working memory task, sensitive to hippocampal lesions, reveals age-dependent and age-independent behavioural changes in a mouse model of amyloid pathology.

Three experiments examined the ability of mice to forage efficiently for liquid rewards in pots located in an open field arena. Search behaviour was unconstrained other than by the walls of the arena. All mice acquired the task within 4 days of training, with one trial per day. Experiment 1 tested the hypothesis that hippocampal lesions would disrupt foraging behaviour using extramaze cues. Mice with hippocampal lesions showed normal latency to initiate foraging and to complete the task relative to sham-operated mice. However, lesioned mice showed increased perseverative responding (sensitization) to recently rewarded locations, increased total working memory errors and an increased propensity to search near previously rewarded locations. In Experiment 2, the extramaze cues were obscured and each pot was identified by a unique pattern. Under these conditions, mice with hippocampal lesions showed comparable working memory errors to control mice. However, lesioned mice continued to display increased perseverative responding and altered search strategies. Experiment 3 tested the hypothesis that age-related accumulation of amyloid would disrupt foraging behaviour in transgenic PDAPP mice expressing the V717F amyloid precursor protein (APP) mutation. Consistent with previous findings, PDAPP mice showed both age-dependent and age-independent behavioural changes. More specifically, 14-16 month-old PDAPP mice showed a deficit in perseverative responding and working memory errors. In contrast, changes in search behaviour, such as systematic circling, were present throughout development. The latter indicates that APP overexpression contributed to some features of the PDAPP behavioural phenotype, whereas working memory and flexible responding was sensitive to ageing and ß-amyloid burden. In conclusion, the present study provided novel insight into the role of the hippocampus and the effects of APP overexpression on memory and search behaviour in an open-field foraging task.

BET bromodomain ligands: Probing the WPF shelf to improve BRD4 bromodomain affinity and metabolic stability.

Ligands for the bromodomain and extra-terminal domain (BET) family of bromodomains have shown promise as useful therapeutic agents for treating a range of cancers and inflammation. Here we report that our previously developed 3,5-dimethylisoxazole-based BET bromodomain ligand (OXFBD02) inhibits interactions of BRD4(1) with the RelA subunit of NF-κB, in addition to histone H4. This ligand shows a promising profile in a screen of the NCI-60 panel but was rapidly metabolised (t½â€¯= 39.8 min). Structure-guided optimisation of compound properties led to the development of the 3-pyridyl-derived OXFBD04. Molecular dynamics simulations assisted our understanding of the role played by an internal hydrogen bond in altering the affinity of this series of molecules for BRD4(1). OXFBD04 shows improved BRD4(1) affinity (IC50 = 166 nM), optimised physicochemical properties (LE = 0.43; LLE = 5.74; SFI = 5.96), and greater metabolic stability (t½â€¯= 388 min).

Systems Analysis of the Complement-Induced Priming Phase of Liver Regeneration.

Liver regeneration is a well-orchestrated process in the liver that allows mature hepatocytes to reenter the cell cycle to proliferate and replace lost or damaged cells. This process is often impaired in fatty or diseased livers, leading to cirrhosis and other deleterious phenotypes. Prior research has established the role of the complement system and its effector proteins in the progression of liver regeneration; however, a detailed mechanistic understanding of the involvement of complement in regeneration is yet to be established. In this study, we have examined the role of the complement system during the priming phase of liver regeneration through a systems level analysis using a combination of transcriptomic and metabolomic measurements. More specifically, we have performed partial hepatectomy on mice with genetic deficiency in C3, the major component of the complement cascade, and collected their livers at various time points. Based on our analysis, we show that the C3 cascade activates c-fos and promotes the TNF-α signaling pathway, which then activates acute-phase genes such as serum amyloid proteins and orosomucoids. The complement activation also regulates the efflux and the metabolism of cholesterol, an important metabolite for cell cycle and proliferation. Based on our systems level analysis, we provide an integrated model for the complement-induced priming phase of liver regeneration.

RhoC GTPase Is a Potent Regulator of Glutamine Metabolism and N-Acetylaspartate Production in Inflammatory Breast Cancer Cells.

Inflammatory breast cancer (IBC) is an extremely lethal cancer that rapidly metastasizes. Although the molecular attributes of IBC have been described, little is known about the underlying metabolic features of the disease. Using a variety of metabolic assays, including (13)C tracer experiments, we found that SUM149 cells, the primary in vitro model of IBC, exhibit metabolic abnormalities that distinguish them from other breast cancer cells, including elevated levels of N-acetylaspartate, a metabolite primarily associated with neuronal disorders and gliomas. Here we provide the first evidence of N-acetylaspartate in breast cancer. We also report that the oncogene RhoC, a driver of metastatic potential, modulates glutamine and N-acetylaspartate metabolism in IBC cells in vitro, revealing a novel role for RhoC as a regulator of tumor cell metabolism that extends beyond its well known role in cytoskeletal rearrangement.