Novel aldo-keto reductase 1C3 inhibitor affects androgen metabolism but not ovarian function in healthy women: a phase 1 study.

OBJECTIVE: Aldo-keto reductase 1C3 (AKR1C3) has been postulated to be involved in androgen, progesterone, and estrogen metabolism. Aldo-keto reductase 1C3 inhibition has been proposed for treatment of endometriosis and polycystic ovary syndrome. Clinical biomarkers of target engagement, which can greatly facilitate drug development, have not yet been described for AKR1C3 inhibitors. Here, we analyzed pharmacodynamic data from a phase 1 study with a new selective AKR1C3 inhibitor, BAY1128688, to identify response biomarkers and assess effects on ovarian function. DESIGN: In a multiple-ascending-dose placebo-controlled study, 33 postmenopausal women received BAY1128688 (3, 30, or 90 mg once daily or 60 mg twice daily) or placebo for 14 days. Eighteen premenopausal women received 60 mg BAY1128688 once or twice daily for 28 days. METHODS: We measured 17 serum steroids by liquid chromatography-tandem mass spectrometry, alongside analysis of pharmacokinetics, menstrual cyclicity, and safety parameters. RESULTS: In both study populations, we observed substantial, dose-dependent increases in circulating concentrations of the inactive androgen metabolite androsterone and minor increases in circulating etiocholanolone and dihydrotestosterone concentrations. In premenopausal women, androsterone concentrations increased 2.95-fold on average (95% confidence interval: 0.35-3.55) during once- or twice-daily treatment. Note, no concomitant changes in serum 17ß-estradiol and progesterone were observed, and menstrual cyclicity and ovarian function were not altered by the treatment. CONCLUSIONS: Serum androsterone was identified as a robust response biomarker for AKR1C3 inhibitor treatment in women. Aldo-keto reductase 1C3 inhibitor administration for 4 weeks did not affect ovarian function.ClinicalTrials.gov Identifier: NCT02434640; EudraCT Number: 2014-005298-36.

Connecting real-world digital mobility assessment to clinical outcomes for regulatory and clinical endorsement-the Mobilise-D study protocol.

BACKGROUND: The development of optimal strategies to treat impaired mobility related to ageing and chronic disease requires better ways to detect and measure it. Digital health technology, including body worn sensors, has the potential to directly and accurately capture real-world mobility. Mobilise-D consists of 34 partners from 13 countries who are working together to jointly develop and implement a digital mobility assessment solution to demonstrate that real-world digital mobility outcomes have the potential to provide a better, safer, and quicker way to assess, monitor, and predict the efficacy of new interventions on impaired mobility. The overarching objective of the study is to establish the clinical validity of digital outcomes in patient populations impacted by mobility challenges, and to support engagement with regulatory and health technology agencies towards acceptance of digital mobility assessment in regulatory and health technology assessment decisions. METHODS/DESIGN: The Mobilise-D clinical validation study is a longitudinal observational cohort study that will recruit 2400 participants from four clinical cohorts. The populations of the Innovative Medicine Initiative-Joint Undertaking represent neurodegenerative conditions (Parkinson's Disease), respiratory disease (Chronic Obstructive Pulmonary Disease), neuro-inflammatory disorder (Multiple Sclerosis), fall-related injuries, osteoporosis, sarcopenia, and frailty (Proximal Femoral Fracture). In total, 17 clinical sites in ten countries will recruit participants who will be evaluated every six months over a period of two years. A wide range of core and cohort specific outcome measures will be collected, spanning patient-reported, observer-reported, and clinician-reported outcomes as well as performance-based outcomes (physical measures and cognitive/mental measures). Daily-living mobility and physical capacity will be assessed directly using a wearable device. These four clinical cohorts were chosen to obtain generalizable clinical findings, including diverse clinical, cultural, geographical, and age representation. The disease cohorts include a broad and heterogeneous range of subject characteristics with varying chronic care needs, and represent different trajectories of mobility disability. DISCUSSION: The results of Mobilise-D will provide longitudinal data on the use of digital mobility outcomes to identify, stratify, and monitor disability. This will support the development of widespread, cost-effective access to optimal clinical mobility management through personalised healthcare. Further, Mobilise-D will provide evidence-based, direct measures which can be endorsed by regulatory agencies and health technology assessment bodies to quantify the impact of disease-modifying interventions on mobility. TRIAL REGISTRATION: ISRCTN12051706.

Real-Life Multimarker Monitoring in Patients with Heart Failure: Continuous Remote Monitoring of Mobility and Patient-Reported Outcomes as Digital End Points in Future Heart-Failure Trials.

AIMS: Heart failure (HF) affects approximately 26 million people worldwide. With an aging global population, innovative approaches to HF evaluation and management are needed to cope with the worsening HF epidemic. The aim of the Real-Life Multimarker Monitoring in Patients with Heart Failure (REALIsM-HF) study (NCT03507439) is to evaluate a composite instrument comprising remote, real-time, activity-monitoring devices combined with daily electronic patient-reported outcome (ePRO) items in patients who have been hospitalized for HF and are undergoing standard HF assessment (e.g., 6-min walking distance [6MWD], blood biomarkers, Kansas City Cardiomyopathy Questionnaire [KCCQ], and echocardiography). METHODS: REALIsM-HF is an ongoing, 12-week, observational study enrolling 80-100 patients aged ≥45 years with HF with preserved ejection fraction (HFpEF; EF ≥45%) or reduced EF (HFrEF; EF ≤35%). Statistical analyses will include examining the association between data from wearables (the AVIVO© mobile patient management patch or VitalPatch© biosensor, and the DynaPort MoveMonitor©), daily ePROs, and conventional HF metrics (e.g., serum/plasma biomarkers, 6MWD, KCCQ, and echocardiographic parameters). The feasibility of and patient compliance with at-home devices will be documented, and the data captured for the purpose of establishing reference values in patients with HFpEF or HFrEF will be summarized. CONCLUSIONS: The REALIsM-HF study is to evaluate the longitudinal daily activity profiles of patients with HF and correlate these with changes in serum/plasma biomarker profiles, symptoms, quality of life, and cardiac function and morphology to inform the use of wearable activity monitors for developing novel therapies and managing patients.

Walking-related digital mobility outcomes as clinical trial endpoint measures: protocol for a scoping review.

INTRODUCTION: Advances in wearable sensor technology now enable frequent, objective monitoring of real-world walking. Walking-related digital mobility outcomes (DMOs), such as real-world walking speed, have the potential to be more sensitive to mobility changes than traditional clinical assessments. However, it is not yet clear which DMOs are most suitable for formal validation. In this review, we will explore the evidence on discriminant ability, construct validity, prognostic value and responsiveness of walking-related DMOs in four disease areas: Parkinson's disease, multiple sclerosis, chronic obstructive pulmonary disease and proximal femoral fracture. METHODS AND ANALYSIS: Arksey and O'Malley's methodological framework for scoping reviews will guide study conduct. We will search seven databases (Medline, CINAHL, Scopus, Web of Science, EMBASE, IEEE Digital Library and Cochrane Library) and grey literature for studies which (1) measure differences in DMOs between healthy and pathological walking, (2) assess relationships between DMOs and traditional clinical measures, (3) assess the prognostic value of DMOs and (4) use DMOs as endpoints in interventional clinical trials. Two reviewers will screen each abstract and full-text manuscript according to predefined eligibility criteria. We will then chart extracted data, map the literature, perform a narrative synthesis and identify gaps. ETHICS AND DISSEMINATION: As this review is limited to publicly available materials, it does not require ethical approval. This work is part of Mobilise-D, an Innovative Medicines Initiative Joint Undertaking which aims to deliver, validate and obtain regulatory approval for DMOs. Results will be shared with the scientific community and general public in cooperation with the Mobilise-D communication team. REGISTRATION: Study materials and updates will be made available through the Center for Open Science's OSFRegistry (https://osf.io/k7395).

Single-agent activity of phosphatidylinositol 3-kinase inhibition with copanlisib in patients with molecularly defined relapsed or refractory diffuse large B-cell lymphoma.

Patients with relapsed/refractory diffuse large B-cell lymphoma (DLBCL) have adverse outcomes. We evaluated the efficacy and safety of the phosphatidylinositol 3-kinase inhibitor copanlisib in patients with relapsed/refractory DLBCL and assessed the relationship between efficacy and DLBCL cell of origin (COO; activated B-cell like [ABC] and germinal center B-cell like [GCB]) and other biomarkers. The primary endpoint was objective response rate (ORR) in DLBCL COO subgroups (ABC, GCB, and unclassifiable) and by CD79B mutational status (NCT02391116). Sixty-seven patients received copanlisib (ABC DLBCL, n = 19; GCB DLBCL, n = 30; unclassifiable, n = 3; missing, n = 15). The ORR was 19.4%; 31.6% and 13.3% in ABC and GCB DLBCL patients, respectively. ORR was 22.2%/20.0% for patients with/without CD79B mutations (wild type, n = 45; mutant, n = 9; missing, n = 13). Overall median progression-free survival and duration of response were 1.8 and 4.3 months, respectively. Adverse events included hypertension (40.3%), diarrhea (37.3%), and hyperglycemia (32.8%). Aberrations were detected in 338 genes, including BCL2 (53.7%) and MLL2 (53.7%). A 16-gene signature separating responders from nonresponders was identified. Copanlisib treatment demonstrated a manageable safety profile in patients with relapsed/refractory DLBCL and a numerically higher response rate in ABC vs. GCB DLBCL patients.

A Roadmap to Inform Development, Validation and Approval of Digital Mobility Outcomes: The Mobilise-D Approach.

Health care has had to adapt rapidly to COVID-19, and this in turn has highlighted a pressing need for tools to facilitate remote visits and monitoring. Digital health technology, including body-worn devices, offers a solution using digital outcomes to measure and monitor disease status and provide outcomes meaningful to both patients and health care professionals. Remote monitoring of physical mobility is a prime example, because mobility is among the most advanced modalities that can be assessed digitally and remotely. Loss of mobility is also an important feature of many health conditions, providing a read-out of health as well as a target for intervention. Real-world, continuous digital measures of mobility (digital mobility outcomes or DMOs) provide an opportunity for novel insights into health care conditions complementing existing mobility measures. Accepted and approved DMOs are not yet widely available. The need for large collaborative efforts to tackle the critical steps to adoption is widely recognised. Mobilise-D is an example. It is a multidisciplinary consortium of 34 institutions from academia and industry funded through the European Innovative Medicines Initiative 2 Joint Undertaking. Members of Mobilise-D are collaborating to address the critical steps for DMOs to be adopted in clinical trials and ultimately health care. To achieve this, the consortium has developed a roadmap to inform the development, validation and approval of DMOs in Parkinson's disease, multiple sclerosis, chronic obstructive pulmonary disease and recovery from proximal femoral fracture. Here we aim to describe the proposed approach and provide a high-level view of the ongoing and planned work of the Mobilise-D consortium. Ultimately, Mobilise-D aims to stimulate widespread adoption of DMOs through the provision of device agnostic software, standards and robust validation in order to bring digital outcomes from concept to use in clinical trials and health care.

mTORC1 Inhibition Corrects Neurodevelopmental and Synaptic Alterations in a Human Stem Cell Model of Tuberous Sclerosis.

Hyperfunction of the mTORC1 pathway has been associated with idiopathic and syndromic forms of autism spectrum disorder (ASD), including tuberous sclerosis, caused by loss of either TSC1 or TSC2. It remains largely unknown how developmental processes and biochemical signaling affected by mTORC1 dysregulation contribute to human neuronal dysfunction. Here, we have characterized multiple stages of neurogenesis and synapse formation in human neurons derived from TSC2-deleted pluripotent stem cells. Homozygous TSC2 deletion causes severe developmental abnormalities that recapitulate pathological hallmarks of cortical malformations in patients. Both TSC2(+/-) and TSC2(-/-) neurons display altered synaptic transmission paralleled by molecular changes in pathways associated with autism, suggesting the convergence of pathological mechanisms in ASD. Pharmacological inhibition of mTORC1 corrects developmental abnormalities and synaptic dysfunction during independent developmental stages. Our results uncouple stage-specific roles of mTORC1 in human neuronal development and contribute to a better understanding of the onset of neuronal pathophysiology in tuberous sclerosis.

Remodeling of plasma lipoproteins in patients with rheumatoid arthritis: Interleukin-6 receptor-alpha inhibition with tocilizumab.

PURPOSE: Rheumatoid arthritis (RA) is associated with increased cardiovascular risk, mediated in part by elevated circulating interleukin-6 levels and proinflammatory changes in plasma lipoproteins. We hypothesized that RA patients acquire inflammation-induced modifications to the protein cargo of circulating lipoproteins that may be reversed by tocilizumab, an interleukin-6 receptor-alpha inhibitor. EXPERIMENTAL DESIGN: Size-exclusion chromatography and reverse-phase protein arrays using 29 antibodies against 26 proteins were applied at baseline and after tocilizumab treatment to analyze the distributions of apolipoproteins, enzymes, lipid transfer proteins, and other associated proteins in plasma lipoprotein fractions from 20 women with RA. RESULTS: A 30% reduction in high-density lipoprotein (HDL)-associated serum amyloid A4 and complement C4 occurred with tocilizumab. Levels of C-reactive protein, associated or comigrating with HDL and low-density lipoprotein (LDL) peaks, were reduced on treatment by approximately 80% and 24%, respectively. Reductions in lipoprotein-associated phospholipase A2, lipoprotein (a), and cholesteryl ester transfer protein in the LDL fraction suggest reductions in LDL-associated proatherogenic factors. Elevations in very low-density lipoprotein (VLDL) enriched with apolipoprotein E were equally observed. CONCLUSIONS AND CLINICAL RELEVANCE: Tocilizumab treatment led to reductions in proinflammatory components and proatherogenic proteins associated with HDL. Whether changes in the proteome of VLDL, LDL, and HDL induced by anti-inflammatory tocilizumab treatment in RA patients modify cardiovascular disease risk requires further investigation.

Characterization of a human pluripotent stem cell-derived model of neuronal development using multiplexed targeted proteomics.

PURPOSE: Human pluripotent stem cell (hPSC)-derived cellular models have great potential to enable drug discovery and improve translation of preclinical insights to the clinic. We have developed a hPSC-derived neural precursor cell model for studying early events in human brain development. We present protein-level characterization of this model, using a multiplexed SRM approach, to establish reproducibility and physiological relevance; essential prerequisites for utilization of the neuronal development model in phenotypic screening-based drug discovery. EXPERIMENTAL DESIGN: Profiles of 246 proteins across three key stages of in vitro neuron differentiation were analyzed by SRM. Three independently hPSC-derived isogenic neural stem cell (NSC) lines were analyzed across five to nine independent neuronal differentiations. RESULTS: One hundred seventy-five proteins were reliably quantified revealing a time-dependent pattern of protein regulation that reflected protein dynamics during in vivo brain development and that was conserved across replicate differentiations and multiple cell lines. CONCLUSIONS AND CLINICAL RELEVANCE: SRM-based protein profiling enabled establishment of the reproducibility and physiological relevance of the hPSC-derived neuronal model. Combined with the successful quantification of proteins relevant to neurodevelopmental diseases, this validates the platform for use as a model to enable neuroscience drug discovery.

Automated sample preparation platform for mass spectrometry-based plasma proteomics and biomarker discovery.

The identification of novel biomarkers from human plasma remains a critical need in order to develop and monitor drug therapies for nearly all disease areas. The discovery of novel plasma biomarkers is, however, significantly hampered by the complexity and dynamic range of proteins within plasma, as well as the inherent variability in composition from patient to patient. In addition, it is widely accepted that most soluble plasma biomarkers for diseases such as cancer will be represented by tissue leakage products, circulating in plasma at low levels. It is therefore necessary to find approaches with the prerequisite level of sensitivity in such a complex biological matrix. Strategies for fractionating the plasma proteome have been suggested, but improvements in sensitivity are often negated by the resultant process variability. Here we describe an approach using multidimensional chromatography and on-line protein derivatization, which allows for higher sensitivity, whilst minimizing the process variability. In order to evaluate this automated process fully, we demonstrate three levels of processing and compare sensitivity, throughput and reproducibility. We demonstrate that high sensitivity analysis of the human plasma proteome is possible down to the low ng/mL or even high pg/mL level with a high degree of technical reproducibility.

Performance of PLS regression coefficients in selecting variables for each response of a multivariate PLS for omics-type data.

Multivariate partial least square (PLS) regression allows the modeling of complex biological events, by considering different factors at the same time. It is unaffected by data collinearity, representing a valuable method for modeling high-dimensional biological data (as derived from genomics, proteomics and peptidomics). In presence of multiple responses, it is of particular interest how to appropriately "dissect" the model, to reveal the importance of single attributes with regard to individual responses (for example, variable selection). In this paper, performances of multivariate PLS regression coefficients, in selecting relevant predictors for different responses in omics-type of data, were investigated by means of a receiver operating characteristic (ROC) analysis. For this purpose, simulated data, mimicking the covariance structures of microarray and liquid chromatography mass spectrometric data, were used to generate matrices of predictors and responses. The relevant predictors were set a priori. The influences of noise, the source of data with different covariance structure and the size of relevant predictors were investigated. Results demonstrate the applicability of PLS regression coefficients in selecting variables for each response of a multivariate PLS, in omics-type of data. Comparisons with other feature selection methods, such as variable importance in the projection scores, principal component regression, and least absolute shrinkage and selection operator regression were also provided.

Proteolysis in model sourdough fermentations.

Model wheat doughs started with six different lactic acid bacteria (LAB), with or without a commercial baker's yeast culture, were used to study proteolysis in sourdough fermentations. Cell counts, pH, and free amino acid concentration were measured. Sequential extraction of dough samples was performed to separate wheat proteins. The salt-soluble protein fraction (albumins and globulins) was analyzed by RP-HPLC and SDS-PAGE, whereas propanol-soluble (gliadins) and insoluble (glutenins) protein fractions were analyzed by SDS-PAGE only. Multivariate statistical methods were used for the analysis of results. The presence of yeasts and LAB affected RP-HPLC and SDS-PAGE patterns of the salt-soluble fraction in a complex way. The only changes in the gluten proteins that could be related to the presence of LAB were the appearance of new protein fragments (20 and 27 kDa) from gliadins and the degradation of high molecular weight glutenin subunits.

Phenotypic characterization of lactic acid bacteria from sourdoughs for Altamura bread produced in Apulia (Southern Italy).

In order to study the composition of the lactic acid bacteria (LAB) community of sourdoughs used for the manufacture of Altamura bread, a traditional durum wheat bread produced in Apulia (Southern Italy), 111 strains of LAB were isolated and characterized. The phenotypic characterization of the isolates, carried out using a set of 29 tests, allowed the identification of 15 clusters at the 80% similarity level by hierarchical cluster analysis. Of the isolates, 88% were identified as facultatively heterofermentative LAB (Lactobacillus plantarum, Lb. paracasei, Lb. casei) and 12% as heterofermentative LAB (Lb. brevis, Leuconostoc mesenteroides). SDS-PAGE profiles of whole cell proteins of 68 strains confirmed the identification. Both the diversity and structure of the lactic microflora for sourdoughs for Altamura bread varied among samples.

Processing of chromatographic data for chemometric analysis of peptide profiles from cheese extracts: a novel approach.

Chemometric analysis of chromatograms plays a fundamental role in characterization of foods or in detection of adulteration. Data for multivariate analysis of chromatographic profiles are usually obtained by visual matching (VM) of peaks, the identities of which, as for peptide profiles from cheese extracts, are often unknown. To avoid the main disadvantages of VM, which is subjective and time-consuming, a novel approach was developed. Fuzzy logic was employed to handle in a systematic way uncertainty in the position of peptide peaks, and chromatograms were processed by a rule-based membership function. Processed data consisted of classes of retention time wherein peak heights were accumulated by using the distance from the center of the class as a weight. The novel approach (fuzzy approach, FA) was compared with VM by using a real data set and by performing multivariate descriptive statistical techniques (principal component analysis, multidimensional scaling, and nonhierarchical cluster analysis). FA provided a fast, reliable, and objective alternative to VM and could be successfully applied for chemometric analysis of chromatographic profiles whenever knowledge of the identity of peaks is lacking or unnecessary.

Overall volumetric oxygen transfer coefficient in an aerated bench-top stirred fermenter in aqueous dispersions of sodium alginate.

The dynamic gassing-out method was used to assess the effect of stirrer speed, air flow rate and alginate concentration on the overall volumetric oxygen mass transfer coefficient (K(L)a) in a bench-top stirred fermenter equipped with two Rushton-type turbines. As the alginate concentration in the medium was increased from 0 to 2% (w/v), the liquid film time constant (tau(F)) increased from approx. 30 to 300% of the electrode time constant (tau(E)), clearly showing that the liquid-probe diffusion film affected the oxygen-electrode response. By accounting for delayed probe response, liquid film time constant and gas residence time, the corrected values of K(L)a were empirically correlated with the above independent variables as converted into gassed power input and aeration number per unit liquid volume and the effective liquid/water shear viscosity ratio. Despite an average error of 19%, such a correlation might be useful to adjust the oxygen transfer in bench-top stirred reactors during viscous aerobic fermentations.