Objective wearable measures correlate with self-reported chronic pain levels in people with spinal cord stimulation systems.

Spinal Cord Stimulation (SCS) is a well-established therapy for treating chronic pain. However, perceived treatment response to SCS therapy may vary among people with chronic pain due to diverse needs and backgrounds. Patient Reported Outcomes (PROs) from standard survey questions do not provide the full picture of what has happened to a patient since their last visit, and digital PROs require patients to visit an app or otherwise regularly engage with software. This study aims to assess the feasibility of using digital biomarkers collected from wearables during SCS treatment to predict pain and PRO outcomes. Twenty participants with chronic pain were recruited and implanted with SCS. During the six months of the study, activity and physiological metrics were collected and data from 15 participants was used to develop a machine learning pipeline to objectively predict pain levels and categories of PRO measures. The model reached an accuracy of 0.768 ± 0.012 in predicting the pain intensity of mild, moderate, and severe. Feature importance analysis showed that digital biomarkers from the smartwatch such as heart rate, heart rate variability, step count, and stand time can contribute to modeling different aspects of pain. The results of the study suggest that wearable biomarkers can be used to predict therapy outcomes in people with chronic pain, enabling continuous, real-time monitoring of patients during the use of implanted therapies.

Core patient-reported outcome measures for chronic pain patients treated with spinal cord stimulation or dorsal root ganglia stimulation.

BACKGROUND: Neurostimulation is a highly effective therapy for the treatment of chronic Intractable pain, however, due to the complexity of pain, measuring a subject's long-term response to the therapy remains difficult. Frequent measurement of patient-reported outcomes (PROs) to reflect multiple aspects of subjects' pain is a crucial step in determining therapy outcomes. However, collecting full-length PROs is burdensome for both patients and clinicians. The objective of this work is to identify the reduced set of questions from multiple validated PROs that can accurately characterize chronic pain patients' responses to neurostimulation therapies. METHODS: Validated PROs were used to capture pain, physical function and disability, as well as psychometric, satisfaction, and global health metrics. PROs were collected from 509 patients implanted with Spinal Cord Stimulation (SCS) or Dorsal Root Ganglia (DRG) neurostimulators enrolled in the prospective, international, post-market REALITY study (NCT03876054, Registration Date: March 15, 2019). A combination of linear regression, Pearson's correlation, and factor analysis were used to eliminate highly correlated questions and find the minimal meaningful set of questions within the predefined domains of each scale. RESULTS: The shortened versions of the questionnaires presented almost identical accuracy for classifying the therapy outcomes as compared to the validated full-length versions. In addition, principal component analysis was performed on all the PROs and showed a robust clustering of pain intensity, psychological factors, physical function, and sleep across multiple PROs. A selected set of questions captured from multiple PROs can provide adequate information for measuring neurostimulation therapy outcomes. CONCLUSIONS: PROs are important subjective measures to evaluate the physiological and psychological aspects of pain. However, these measures are cumbersome to collect. These shorter and more targeted PROs could result in better patient engagement, and enhanced and more frequent data collection processes for digital health platforms that minimize patient burden while increasing therapeutic benefits for chronic pain patients.

Objective wearable measures and subjective questionnaires for predicting response to neurostimulation in people with chronic pain.

BACKGROUND: Neurostimulation is an effective therapy for treating and management of refractory chronic pain. However, the complex nature of pain and infrequent in-clinic visits, determining subject's long-term response to the therapy remains difficult. Frequent measurement of pain in this population can help with early diagnosis, disease progression monitoring, and evaluating long-term therapeutic efficacy. This paper compares the utilization of the common subjective patient-reported outcomes with objective measures captured through a wearable device for predicting the response to neurostimulation therapy. METHOD: Data is from the ongoing international prospective post-market REALITY clinical study, which collects long-term patient-reported outcomes from 557 subjects implanted by Spinal Cord Stimulator (SCS) or Dorsal Root Ganglia (DRG) neurostimulators. The REALITY sub-study was designed for collecting additional wearables data on a subset of 20 participants implanted with SCS devices for up to six months post implantation. We first implemented a combination of dimensionality reduction algorithms and correlation analyses to explore the mathematical relationships between objective wearable data and subjective patient-reported outcomes. We then developed machine learning models to predict therapy outcome based on the subject's response to the numerical rating scale (NRS) or patient global impression of change (PGIC). RESULTS: Principal component analysis showed that psychological aspects of pain were associated with heart rate variability, while movement-related measures were strongly associated with patient-reported outcomes related to physical function and social role participation. Our machine learning models using objective wearable data predicted PGIC and NRS outcomes with high accuracy without subjective data. The prediction accuracy was higher for PGIC compared with the NRS using subjective-only measures primarily driven by the patient satisfaction feature. Similarly, the PGIC questions reflect an overall change since the study onset and could be a better predictor of long-term neurostimulation therapy outcome. CONCLUSIONS: The significance of this study is to introduce a novel use of wearable data collected from a subset of patients to capture multi-dimensional aspects of pain and compare the prediction power with the subjective data from a larger data set. The discovery of pain digital biomarkers could result in a better understanding of the patient's response to therapy and their general well-being.

Inhibition of protein disulfide isomerase in glioblastoma causes marked downregulation of DNA repair and DNA damage response genes.

Aberrant overexpression of endoplasmic reticulum (ER)-resident oxidoreductase protein disulfide isomerase (PDI) plays an important role in cancer progression. In this study, we demonstrate that PDI promotes glioblastoma (GBM) cell growth and describe a class of allosteric PDI inhibitors that are selective for PDI over other PDI family members. Methods: We performed a phenotypic screening triage campaign of over 20,000 diverse compounds to identify PDI inhibitors cytotoxic to cancer cells. From this screen, BAP2 emerged as a lead compound, and we assessed BAP2-PDI interactions with gel filtration, thiol-competition assays, and site-directed mutagenesis studies. To assess selectivity, we compared BAP2 activity across several PDI family members in the PDI reductase assay. Finally, we performed in vivo studies with a mouse xenograft model of GBM combining BAP2 and the standard of care (temozolomide and radiation), and identified affected gene pathways with nascent RNA sequencing (Bru-seq). Results: BAP2 and related analogs are novel PDI inhibitors that selectively inhibit PDIA1 and PDIp. Though BAP2 contains a weak Michael acceptor, interaction with PDI relies on Histidine 256 in the b' domain of PDI, suggesting allosteric binding. Furthermore, both in vitro and in vivo, BAP2 reduces cell and tumor growth. BAP2 alters the transcription of genes involved in the unfolded protein response, ER stress, apoptosis and DNA repair response. Conclusion: These results indicate that BAP2 has anti-tumor activity and the suppressive effect on DNA repair gene expression warrants combination with DNA damaging agents to treat GBM.

Design, Synthesis, and Biological Evaluation of Novel Allosteric Protein Disulfide Isomerase Inhibitors.

Protein disulfide isomerase (PDI) is responsible for nascent protein folding in the endoplasmic reticulum (ER) and is critical for glioblastoma survival. To improve the potency of lead PDI inhibitor BAP2 (( E)-3-(3-(4-hydroxyphenyl)-3-oxoprop-1-en-1-yl)benzonitrile), we designed and synthesized 67 analogues. We determined that PDI inhibition relied on the A ring hydroxyl group of the chalcone scaffold and cLogP increase in the sulfonamide chain improved potency. Docking studies revealed that BAP2 and analogues bind to His256 in the b' domain of PDI, and mutation of His256 to Ala abolishes BAP2 analogue activity. BAP2 and optimized analogue 59 have modest thiol reactivity; however, we propose that PDI inhibition by BAP2 analogues depends on the b' domain. Importantly, analogues inhibit glioblastoma cell growth, induce ER stress, increase expression of G2M checkpoint proteins, and reduce expression of DNA repair proteins. Cumulatively, our results support inhibition of PDI as a novel strategy to treat glioblastoma.

Discovery and Mechanistic Elucidation of a Class of Protein Disulfide Isomerase Inhibitors for the Treatment of Glioblastoma.

Protein disulfide isomerase (PDI) is overexpressed in glioblastoma, the most aggressive form of brain cancer, and folds nascent proteins responsible for the progression and spread of the disease. Herein we describe a novel nanomolar PDI inhibitor, pyrimidotriazinedione 35G8, that is toxic in a panel of human glioblastoma cell lines. We performed a medium-throughput 20 000-compound screen of a diverse subset of 1 000 000 compounds to identify cytotoxic small molecules. Cytotoxic compounds were screened for PDI inhibition, and, from the screen, 35G8 emerged as the most cytotoxic inhibitor of PDI. Bromouridine labeling and sequencing (Bru-seq) of nascent RNA revealed that 35G8 induces nuclear factor-like 2 (Nrf2) antioxidant response, endoplasmic reticulum (ER) stress response, and autophagy. Specifically, 35G8 upregulated heme oxygenase 1 and solute carrier family 7 member 11 (SLC7A11) transcription and protein expression and repressed PDI target genes such as thioredoxin-interacting protein 1 (TXNIP) and early growth response 1 (EGR1). Interestingly, 35G8-induced cell death did not proceed via apoptosis or necrosis, but by a mixture of autophagy and ferroptosis. Cumulatively, our data demonstrate a mechanism for a novel PDI inhibitor as a chemical probe to validate PDI as a target for brain cancer.

Mechanistic evaluation and transcriptional signature of a glutathione S-transferase omega 1 inhibitor.

Glutathione S-transferase omega 1 (GSTO1) is an atypical GST isoform that is overexpressed in several cancers and has been implicated in drug resistance. Currently, no small-molecule drug targeting GSTO1 is under clinical development. Here we show that silencing of GSTO1 with siRNA significantly impairs cancer cell viability, validating GSTO1 as a potential new target in oncology. We report on the development and characterization of a series of chloroacetamide-containing potent GSTO1 inhibitors. Co-crystal structures of GSTO1 with our inhibitors demonstrate covalent binding to the active site cysteine. These potent GSTO1 inhibitors suppress cancer cell growth, enhance the cytotoxic effects of cisplatin and inhibit tumour growth in colon cancer models as single agent. Bru-seq-based transcription profiling unravelled novel roles for GSTO1 in cholesterol metabolism, oxidative and endoplasmic stress responses, cytoskeleton and cell migration. Our findings demonstrate the therapeutic utility of GSTO1 inhibitors as anticancer agents and identify the novel cellular pathways under GSTO1 regulation in colorectal cancer.

1H NMR based metabolic profiling in Crohn's disease by random forest methodology.

The present study was designed to search for metabolic biomarkers and their correlation with serum zinc in Crohn's disease patients. Crohn's disease (CD) is a form of inflammatory bowel disease that may affect any part of the gastrointestinal tract and can be difficult to diagnose using the clinical tests. Thus, introduction of a novel diagnostic method would be a major step towards CD treatment. Proton nuclear magnetic resonance spectroscopy ((1)H NMR) was employed for metabolic profiling to find out which metabolites in the serum have meaningful significance in the diagnosis of CD. CD and healthy subjects were correctly classified using random forest methodology. The classification model for the external test set showed a 94% correct classification of CD and healthy subjects. The present study suggests Valine and Isoleucine as differentiating metabolites for CD diagnosis. These metabolites can be used for screening of risky samples at the early stages of CD diagnoses. Moreover, a robust random forest regression model with good prediction outcomes was developed for correlating serum zinc level and metabolite concentrations. The regression model showed the correlation (R(2)) and root mean square error values of 0.83 and 6.44, respectively. This model suggests valuable clues for understanding the mechanism of zinc deficiency in CD patients.

A metabonomics investigation of multiple sclerosis by nuclear magnetic resonance.

Multiple sclerosis (MS) is a nervous system disease that affects the fatty myelin sheaths around the axons of the brain and spinal cord, leading to demyelination and a broad range of signs and symptoms. MS can be difficult to diagnose because its signs and symptoms may be similar to other medical problems. To find out which metabolites in serum are effective for the diagnosis of MS, we utilized metabolic profiling using proton nuclear magnetic resonance spectroscopy ((1)H-NMR). Random forest (RF) was used to classify the MS patients and healthy subjects. Atomic absorption spectroscopy was used to measure the serum levels of selenium. The results showed that the levels of selenium were lower in the MS group, when compared with the control group. RF was used to identify the metabolites that caused selenium changes in people with MS by building a correlation model between these metabolites and serum levels of selenium. For the external test set, the obtained classification model showed a 93% correct classification of MS and healthy subjects. The regression model of levels of selenium and metabolites showed the correlation (R(2)) value of 0.88 for the external test set. The results indicate the suitability of NMR as a screen for identifying MS patients and healthy subjects. A novel model with good prediction outcomes was constructed between serum levels of selenium and NMR data.

Is there any interaction between telomeric DNA structures, G-quadruplex and I-motif, with saffron active metabolites?

Telomeric DNA contains some unique secondary structures, such as G-quadruplex and I-motif. These structures may be stabilized or changed by binding to specific proteins or small molecules. Herein, we report the in vitro effect of crocin, crocetin, picrocrocin, and safranal on these structures. Circular dichroism (CD) data indicate that crocetin has higher affinity for these structures. Safranal and crocin induce little change in the I-motif and G-quadruplex, respectively. The molecular docking confirms the experimental data and indicates the minor groove binding of ligands with G-quadruplex. The possibility for application of these ligands as sequence-specific drugs should be further investigated.

Quantitative structure-activity relationships and docking studies of calcitonin gene-related peptide antagonists.

Defining the role of calcitonin gene-related peptide in migraine pathogenesis could lead to the application of calcitonin gene-related peptide antagonists as novel migraine therapeutics. In this work, quantitative structure-activity relationship modeling of biological activities of a large range of calcitonin gene-related peptide antagonists was performed using a panel of physicochemical descriptors. The computational studies evaluated different variable selection techniques and demonstrated shuffling stepwise multiple linear regression to be superior over genetic algorithm-multiple linear regression. The linear quantitative structure-activity relationship model revealed better statistical parameters of cross-validation in comparison with the non-linear support vector regression technique. Implementing only five peptide descriptors into this linear quantitative structure-activity relationship model resulted in an extremely robust and highly predictive model with calibration, leave-one-out and leave-20-out validation R(2) of 0.9194, 0.9103, and 0.9214, respectively. We performed docking of the most potent calcitonin gene-related peptide antagonists with the calcitonin gene-related peptide receptor and demonstrated that peptide antagonists act by blocking access to the peptide-binding cleft. We also demonstrated the direct contact of residues 28-37 of the calcitonin gene-related peptide antagonists with the receptor. These results are in agreement with the conclusions drawn from the quantitative structure-activity relationship model, indicating that both electrostatic and steric factors should be taken into account when designing novel calcitonin gene-related peptide antagonists.

New model for polymerization of oligomeric alcohol dehydrogenases into nanoaggregates.

Polymerization and self-assembly of proteins into nanoaggregates of different sizes and morphologies (nanoensembles or nanofilaments) is a phenomenon that involved problems in various neurodegenerative diseases (medicine) and enzyme instability/inactivity (biotechnology). Thermal polymerization of horse liver alcohol dehydrogenase (dimeric) and yeast alcohol dehydrogenase (tetrameric), as biotechnological ADH representative enzymes, was evaluated for the development of a rational strategy to control aggregation. Constructed ADH nuclei, which grew to larger amorphous nanoaggregates, were prevented via high repulsion strain of the net charge values. Good correlation between the variation in scattering and lambda(-2) was related to the amorphousness of the nanoaggregated ADHs, shown by electron microscopic images. Scattering corrections revealed that ADH polymerization was related to the quaternary structural changes, including delocalization of subunits without unfolding, i.e. lacking the 3D conformational and/or secondary-ordered structural changes. The results demonstrated that electrostatic repulsion was not only responsible for disaggregation but also caused a delay in the onset of aggregation temperature, decreasing maximum values of aggregation and amounts of precipitation. Together, our results demonstrate and propose a new model of self-assembly for ADH enzymes based on the construction of nuclei, which grow to formless nanoaggregates with minimal changes in the tertiary and secondary conformations.

Comparative structure-toxicity relationship study of substituted benzenes to Tetrahymena pyriformis using shuffling-adaptive neuro fuzzy inference system and artificial neural networks.

The purpose of this study was to develop the structure-toxicity relationships for a large group of 268 substituted benzene to the ciliate Tetrahymena pyriformis using mechanistically interpretable descriptors. The shuffling-adaptive neuro fuzzy inference system (Shuffling-ANFIS) has been successfully applied to select the important factors affecting the toxicity of substituted benzenes to T. pyriformis. The results of the proposed model were compared with the model of linear-free energy response surface and also the principal component analysis Bayesian-regularized neural network (PCA-BRANN) trained using the same data. The presented model shows a better statistical parameter in comparison with the previous models. The results of the model are promising and descriptive. Five descriptors of octanol-water partition coefficient (logP), bond information content (BIC0), number of R-CX-R (C-026), eigenvalue sum from Z weighted distance matrix (SEigZ) and fragment based polar surface area (PSA) selected by Shuffling-ANFIS reveal the role of hydrophobicity, electronic and steric interactions in the mechanism of toxic action. Sequential zeroing of weights (SZW) as a sensitivity analysis method revealed that the hydrophobicity and electronic interactions play a major role in toxicity of these compounds. Satisfactory results (q(2)=0.828 and RMSE=0.348) in comparison with the previous works indicate that the Shuffling-ANFIS-ANN technique is able to model a diverse chemical class with more than one mechanism of toxicity by using simple and interpretable descriptors. Shuffling-ANFIS can be used as powerful feature selection technique, because its application in prediction of toxicity potency results in good statistical and interpretable physiochemical parameters.

Application of genetic algorithm-kernel partial least square as a novel nonlinear feature selection method: activity of carbonic anhydrase II inhibitors.

This paper introduces the genetic algorithm-kernel partial least square (GA-KPLS), as a novel nonlinear feature selection method. This technique combines genetic algorithms (GAs) as powerful optimization methods with KPLS as a robust nonlinear statistical method for variable selection. This feature selection method is combined with artificial neural network to develop a nonlinear QSAR model for predicting activities of a series of substituted aromatic sulfonamides as carbonic anhydrase II (CA II) inhibitors. Eight simple one- and two-dimensional descriptors were selected by GA-KPLS and considered as inputs for developing artificial neural networks (ANNs). These parameters represent the role of acceptor-donor pair, hydrogen bonding, hydrosolubility and lipophilicity of the active sites and also the size of the inhibitors on inhibitor-isozyme interaction. The accuracy of 8-4-1 networks was illustrated by validation techniques of leave-one-out (LOO) and leave-multiple-out (LMO) cross-validations and Y-randomization. Superiority of this method (GA-KPLS-ANN) over the linear one (MLR) in a previous work and also the GA-PLS-ANN in which a linear feature selection method has been used indicates that the GA-KPLS approach is a powerful method for the variable selection in nonlinear systems.