Predictors of social risk for post-ischemic stroke reintegration.

After stroke rehabilitation, patients need to reintegrate back into their daily life, workplace and society. Reintegration involves complex processes depending on age, sex, stroke severity, cognitive, physical, as well as socioeconomic factors that impact long-term outcomes post-stroke. Moreover, post-stroke quality of life can be impacted by social risks of inadequate family, social, economic, housing and other supports needed by the patients. Social risks and barriers to successful reintegration are poorly understood yet critical for informing clinical or social interventions. Therefore, the aim of this work is to predict social risk at rehabilitation discharge using sociodemographic and clinical variables at rehabilitation admission and identify factors that contribute to this risk. A Gradient Boosting modelling methodology based on decision trees was applied to a Catalan 217-patient cohort of mostly young (mean age 52.7), male (66.4%), ischemic stroke survivors. The modelling task was to predict an individual's social risk upon discharge from rehabilitation based on 16 different demographic, diagnostic and social risk variables (family support, social support, economic status, cohabitation and home accessibility at admission). To correct for imbalance in patient sample numbers with high and low-risk levels (prediction target), five different datasets were prepared by varying the data subsampling methodology. For each of the five datasets a prediction model was trained and the analysis involves a comparison across these models. The training and validation results indicated that the models corrected for prediction target imbalance have similarly good performance (AUC 0.831-0.843) and validation (AUC 0.881 - 0.909). Furthermore, predictor variable importance ranked social support and economic status as the most important variables with the greatest contribution to social risk prediction, however, sex and age had a lesser, but still important, contribution. Due to the complex and multifactorial nature of social risk, factors in combination, including social support and economic status, drive social risk for individuals.

Return to work within a year after first stroke: blue and white collar workers comparison, predictors and causal mediation assessed during inpatient rehabilitation.

BACKGROUND: Most research focuses around impairments in body function and structure, with relatively only a small number exploring their social impact. OBJECTIVES: 1) compare characteristics for individuals who before stroke were blue collar vs. white collar workers 2) identify clinical, functional, and job-related factors associated with return to work within 1 year after discharge 3) identify specific ADL individual items (assessed at rehabilitation discharge) as return to work predictors and 4) identify return to work causal mediators. METHODS: Retrospective observational cohort study, analyzing adult patients with stroke admitted to rehabilitation between 2007 and 2021, including baseline Barthel Index (BI) and return to work assessments between 2008 and 2022. Kaplan-Meier survival curves and Cox proportional hazards were applied. Causal mediation analyses using 1000-bootstrapped simulations were performed. RESULTS: A total of 802 individuals were included (14.6% returned to work), 53.6% blue-collar and 46.4% white-collar. Blue-collar workers showed significantly higher proportion of ischemic stroke, diabetes, dyslipidemia, and hypertension.Individuals not returning to work presented a higher proportion of blue collar, dominant side affected, aphasia, lower BI scores, and larger length of stay (LOS). Multivariable Cox proportional hazards identified age at injury, aphasia, hypertension, and total discharge BI score (C-Index = 0.74). Univariable Cox models identified three independent BI items at all levels of independence: bathing (C-Index = 0.58), grooming (C-Index = 0.56) and feeding (C-Index = 0.59). BI efficiency (gain/LOS) was a causal mediator. CONCLUSION: Blue collar workers showed higher proportion of risk factors and comorbidities. Novel factors, predictors, and a return to work mediator were identified.

Current Topics in Technology-Enabled Stroke Rehabilitation and Reintegration: A Scoping Review and Content Analysis.

BACKGROUND: There is a worldwide health crisis stemming from the rising incidence of various debilitating chronic diseases, with stroke as a leading contributor. Chronic stroke management encompasses rehabilitation and reintegration, and can require decades of personalized medicine and care. Information technology (IT) tools have the potential to support individuals managing chronic stroke symptoms. OBJECTIVES: This scoping review identifies prevalent topics and concepts in research literature on IT technology for stroke rehabilitation and reintegration, utilizing content analysis, based on topic modelling techniques from natural language processing to identify gaps in this literature. ELIGIBILITY CRITERIA: Our methodological search initially identified over 14,000 publications of the last two decades in the Web of Science and Scopus databases, which we filter, using keywords and a qualitative review, to a core corpus of 1062 documents. RESULTS: We generate a 3-topic, 4-topic and 5-topic model and interpret the resulting topics as four distinct thematics in the literature, which we label as Robotics, Software, Functional and Cognitive. We analyze the prevalence and distinctiveness of each thematic and identify some areas relatively neglected by the field. These are mainly in the Cognitive thematic, especially for systems and devices for sensory loss rehabilitation, tasks of daily living performance and social participation. CONCLUSION: The results indicate that IT-enabled stroke literature has focused on Functional outcomes and Robotic technologies, with lesser emphasis on Cognitive outcomes and combined interventions. We hope this review broadens awareness, usage and mainstream acceptance of novel technologies in rehabilitation and reintegration among clinicians, carers and patients.

Long-term trajectories of community integration: identification, characterization, and prediction using inpatient rehabilitation variables.

BACKGROUND: Community integration (CI) is often regarded as the foundation of rehabilitation endeavors after stroke; nevertheless, few studies have investigated the relationship between inpatient rehabilitation (clinical and demographic) variables and long-term CI. OBJECTIVES: To identify novel classes of patients having similar temporal patterns in CI and relate them to baseline features. METHODS: Retrospective observational cohort study analyzing (n = 287) adult patients with stroke admitted to rehabilitation between 2003 and 2018, including baseline Functional Independence Measure (FIM) at discharge, follow-ups (m = 1264) of Community Integration Questionnaire (CIQ) between 2006 and 2022. Growth mixture models (GMMs) were fitted to identify CI trajectories, and baseline predictors were identified using multivariate logistic regression (reporting AUC) with 10-fold cross validation. RESULTS: Each patient was assessed at 2.7 (2.2-3.7), 4.4 (3.7-5.6), and 6.2 (5.4-7.4) years after injury, 66% had a fourth assessment at 7.9 (6.8-8.9) years. GMM identified three classes of trajectories.Lowest CI (n=105, 36.6%): The lowest mean total CIQ; highest proportion of dysphagia (47.6%) and aphasia (46.7%), oldest at injury, largest length of stay (LOS), largest time to admission, and lowest FIM.Highest CI (n=63, 21.9%): The highest mean total CIQ, youngest, shortest LOS, highest education (27% university) highest FIM, and Intermediate CI (n=119, 41.5%): Intermediate mean total CIQ and FIM scores. Age at injury OR: 0.89 (0.85-0.93), FIM OR: 1.04 (1.02-1.07), hypertension OR: 2.86 (1.25-6.87), LOS OR: 0.98 (0.97-0.99), and high education OR: 3.05 (1.22-7.65) predicted highest CI, and AUC was 0.84 (0.76-0.93). CONCLUSION: Novel clinical (e.g. hypertension) and demographic (e.g. education) variables characterized and predicted long-term CI trajectories.

Understanding and Predicting Cognitive Improvement of Young Adults in Ischemic Stroke Rehabilitation Therapy.

Accurate early predictions of a patient's likely cognitive improvement as a result of a stroke rehabilitation programme can assist clinicians in assembling more effective therapeutic programs. In addition, sufficient levels of explainability, which can justify these predictions, are a crucial requirement, as reported by clinicians. This article presents a machine learning (ML) prediction model targeting cognitive improvement after therapy for stroke surviving patients. The prediction model relies on electronic health records from 201 ischemic stroke surviving patients containing demographic information, cognitive assessments at admission from 24 different standardized neuropsychology tests (e.g., TMT, WAIS-III, Stroop, RAVLT, etc.), and therapy information collected during rehabilitation (72,002 entries collected between March 2007 and September 2019). The study population covered young-adult patients with a mean age of 49.51 years and only 4.47% above 65 years of age at the stroke event (no age filter applied). Twenty different classification algorithms (from Python's Scikit-learn library) are trained and evaluated, varying their hyper-parameters and the number of features received as input. Best-performing models reported Recall scores around 0.7 and F1 scores of 0.6, showing the model's ability to identify patients with poor cognitive improvement. The study includes a detailed feature importance report that helps interpret the model's inner decision workings and exposes the most influential factors in the cognitive improvement prediction. The study showed that certain therapy variables (e.g., the proportion of memory and orientation executed tasks) had an important influence on the final prediction of the cognitive improvement of patients at individual and population levels. This type of evidence can serve clinicians in adjusting the therapeutic settings (e.g., type and load of therapy activities) and selecting the one that maximizes cognitive improvement.

Long-term trajectories of motor functional independence after ischemic stroke in young adults: Identification and characterization using inpatient baseline assessments.

BACKGROUND: Stroke is a major worldwide cause of serious long-term disability. Most previous studies addressing functional independence included only inpatients with limited follow-up. OBJECTIVE: To identify novel classes of patients having similar temporal patterns in motor functional independence and relate them to baseline clinical features. METHODS: Retrospective observational cohort study, data were obtained for n = 428 adult patients with ischemic stroke admitted to rehabilitation (March 2005-March 2020), including baseline clinical features and follow-ups of motor Functional Independence Measure (mFIM) categorized as poor, fair or good. Growth mixture models (GMMs) were fitted to identify classes of patients with similar mFIM trajectories. RESULTS: GMM identified three classes of trajectories (1,664 mFIM assessments):C1 (11.2 %), 97.9% having poor admission mFIM, at 4.93 years 61.1% still poor, with the largest percentage of hypertension, neglect, dysphagia, diabetes and dyslipidemia of all three classes.C2 (23.1%), 99% had poor admission mFIM, 25% poor discharge mFIM, the largest percentage of aphasia and greatest mFIM gain, at 4.93 years only 6.2% still poor.C3 (65.7%) the youngest, lowest NIHSS, 37.7% poor admission mFIM, 73% good discharge mFIM, only 4.6% poor discharge mFIM, 90% good at 4.93 years. CONCLUSIONS: GMM identified novel motor functional classes characterized by baseline features.

Differences in Fecal Microbiomes and Metabolomes of People With vs Without Irritable Bowel Syndrome and Bile Acid Malabsorption.

BACKGROUND & AIMS: Irritable bowel syndrome (IBS) is a heterogeneous disorder, but diagnoses and determination of subtypes are made based on symptoms. We profiled the fecal microbiomes of patients with and without IBS to identify biomarkers of this disorder. METHODS: We collected fecal and urine samples from 80 patients with IBS (Rome IV criteria; 16-70 years old) and 65 matched individuals without IBS (control individuals), along with anthropometric, medical, and dietary information. Shotgun and 16S ribosomal RNA amplicon sequencing were performed on feces, whereas urine and fecal metabolites were analyzed by gas chromatography and liquid chromatography-mass spectrometry. Co-occurrence networks were generated based on significant Spearman correlations between data. Bile acid malabsorption (BAM) was identified in patients with diarrhea by retention of radiolabeled selenium-75 homocholic acid taurine. RESULTS: Patients with IBS had significant differences in network connections between diet and fecal microbiomes compared with control individuals; these were accompanied by differences in fecal metabolomes. We did not find significant differences in fecal microbiota composition among patients with different IBS symptom subtypes. Fecal metabolome profiles could discriminate patients with IBS from control individuals. Urine metabolomes also differed significantly between patients with IBS and control individuals, but most discriminatory metabolites were related to diet or medications. Fecal metabolomes, but not microbiomes, could distinguish patients with IBS with vs those without BAM. CONCLUSIONS: Despite the heterogeneity of IBS, patients have significant differences in urine and fecal metabolomes and fecal microbiome vs control individuals, independent of symptom-based subtypes of IBS. Fecal metabolome analysis can be used to distinguish patients with IBS with vs those without BAM. These findings might be used for developing microbe-based treatments for these disorders.

The oral microbiota in colorectal cancer is distinctive and predictive.

BACKGROUND AND AIMS: Microbiota alterations are linked with colorectal cancer (CRC) and notably higher abundance of putative oral bacteria on colonic tumours. However, it is not known if colonic mucosa-associated taxa are indeed orally derived, if such cases are a distinct subset of patients or if the oral microbiome is generally suitable for screening for CRC. METHODS: We profiled the microbiota in oral swabs, colonic mucosae and stool from individuals with CRC (99 subjects), colorectal polyps (32) or controls (103). RESULTS: Several oral taxa were differentially abundant in CRC compared with controls, for example, Streptococcus and Prevotellas pp. A classification model of oral swab microbiota distinguished individuals with CRC or polyps from controls (sensitivity: 53% (CRC)/67% (polyps); specificity: 96%). Combining the data from faecal microbiota and oral swab microbiota increased the sensitivity of this model to 76% (CRC)/88% (polyps). We detected similar bacterial networks in colonic microbiota and oral microbiota datasets comprising putative oral biofilm forming bacteria. While these taxa were more abundant in CRC, core networks between pathogenic, CRC-associated oral bacteria such as Peptostreptococcus, Parvimonas and Fusobacterium were also detected in healthy controls. High abundance of Lachnospiraceae was negatively associated with the colonisation of colonic tissue with oral-like bacterial networks suggesting a protective role for certain microbiota types against CRC, possibly by conferring colonisation resistance to CRC-associated oral taxa and possibly mediated through habitual diet. CONCLUSION: The heterogeneity of CRC may relate to microbiota types that either predispose or provide resistance to the disease, and profiling the oral microbiome may offer an alternative screen for detecting CRC.

Identification of novel molecular signatures of IgA nephropathy through an integrative -omics analysis.

IgA nephropathy (IgAN) is the most prevalent among primary glomerular diseases worldwide. Although our understanding of IgAN has advanced significantly, its underlying biology and potential drug targets are still unexplored. We investigated a combinatorial approach for the analysis of IgAN-relevant -omics data, aiming at identification of novel molecular signatures of the disease. Nine published urinary proteomics datasets were collected and the reported differentially expressed proteins in IgAN vs. healthy controls were integrated into known biological pathways. Proteins participating in these pathways were subjected to multi-step assessment, including investigation of IgAN transcriptomics datasets (Nephroseq database), their reported protein-protein interactions (STRING database), kidney tissue expression (Human Protein Atlas) and literature mining. Through this process, from an initial dataset of 232 proteins significantly associated with IgAN, 20 pathways were predicted, yielding 657 proteins for further analysis. Step-wise evaluation highlighted 20 proteins of possibly high relevance to IgAN and/or kidney disease. Experimental validation of 3 predicted relevant proteins, adenylyl cyclase-associated protein 1 (CAP1), SHC-transforming protein 1 (SHC1) and prolylcarboxypeptidase (PRCP) was performed by immunostaining of human kidney sections. Collectively, this study presents an integrative procedure for -omics data exploitation, giving rise to biologically relevant results.

A simple optogenetic MAPK inhibitor design reveals resonance between transcription-regulating circuitry and temporally-encoded inputs.

Engineering light-sensitive protein regulators has been a tremendous multidisciplinary challenge. Optogenetic regulators of MAPKs, central nodes of cellular regulation, have not previously been described. Here we present OptoJNKi, a light-regulated JNK inhibitor based on the AsLOV2 light-sensor domain using the ubiquitous FMN chromophore. OptoJNKi gene-transfer allows optogenetic applications, whereas protein delivery allows optopharmacology. Development of OptoJNKi suggests a design principle for other optically regulated inhibitors. From this, we generate Optop38i, which inhibits p38MAPK in intact illuminated cells. Neurons are known for interpreting temporally-encoded inputs via interplay between ion channels, membrane potential and intracellular calcium. However, the consequences of temporal variation of JNK-regulating trophic inputs, potentially resulting from synaptic activity and reversible cellular protrusions, on downstream targets are unknown. Using OptoJNKi, we reveal maximal regulation of c-Jun transactivation can occur at unexpectedly slow periodicities of inhibition depending on the inhibitor's subcellular location. This provides evidence for resonance in metazoan JNK-signalling circuits.

Efficient Binding of the NOS1AP C-Terminus to the nNOS PDZ Pocket Requires the Concerted Action of the PDZ Ligand Motif, the Internal ExF Site and Structural Integrity of an Independent Element.

Neuronal nitric oxide synthase is widely regarded as an important contributor to a number of disorders of excitable tissues. Recently the adaptor protein NOS1AP has emerged as a contributor to several nNOS-linked conditions. As a consequence, the unexpectedly complex mechanisms of interaction between nNOS and its effector NOS1AP have become a particularly interesting topic from the point of view of both basic research and the potential for therapeutic applications. Here we demonstrate that the concerted action of two previously described motif regions contributing to the interaction of nNOS with NOS1AP, the ExF region and the PDZ ligand motif, efficiently excludes an alternate ligand from the nNOS-PDZ ligand-binding pocket. Moreover, we identify an additional element with a denaturable structure that contributes to interaction of NOS1AP with nNOS. Denaturation does not affect the functions of the individual motifs and results in a relatively mild drop, ∼3-fold, of overall binding affinity of the C-terminal region of NOS1AP for nNOS. However, denaturation selectively prevents the concerted action of the two motifs that normally results in efficient occlusion of the PDZ ligand-binding pocket, and results in 30-fold reduction of competition between NOS1AP and an alternate PDZ ligand.

Omics databases on kidney disease: where they can be found and how to benefit from them.

In the recent decades, the evolution of omics technologies has led to advances in all biological fields, creating a demand for effective storage, management and exchange of rapidly generated data and research discoveries. To address this need, the development of databases of experimental outputs has become a common part of scientific practice in order to serve as knowledge sources and data-sharing platforms, providing information about genes, transcripts, proteins or metabolites. In this review, we present omics databases available currently, with a special focus on their application in kidney research and possibly in clinical practice. Databases are divided into two categories: general databases with a broad information scope and kidney-specific databases distinctively concentrated on kidney pathologies. In research, databases can be used as a rich source of information about pathophysiological mechanisms and molecular targets. In the future, databases will support clinicians with their decisions, providing better and faster diagnoses and setting the direction towards more preventive, personalized medicine. We also provide a test case demonstrating the potential of biological databases in comparing multi-omics datasets and generating new hypotheses to answer a critical and common diagnostic problem in nephrology practice. In the future, employment of databases combined with data integration and data mining should provide powerful insights into unlocking the mysteries of kidney disease, leading to a potential impact on pharmacological intervention and therapeutic disease management.

The application of multi-omics and systems biology to identify therapeutic targets in chronic kidney disease.

The quest for the ideal therapeutic target in chronic kidney disease (CKD) has been riddled with many obstacles stemming from the molecular complexity of the disease and its co-morbidities. Recent advances in omics technologies and the resulting amount of available data encompassing genomics, proteomics, peptidomics, transcriptomics and metabolomics has created an opportunity for integrating omics datasets to build a comprehensive and dynamic model of the molecular changes in CKD for the purpose of biomarker and drug discovery. This article reviews relevant concepts in omics data integration using systems biology, a mathematical modelling method that globally describes a biological system on the basis of its modules and the functional connections that govern their behaviour. The review describes key databases and bioinformatics tools, as well as the challenges and limitations of the current state of the art, along with practical application to CKD therapeutic target discovery. Moreover, it describes how systems biology and visualization tools can be used to generate clinically relevant molecular models with the capability to identify specific disease pathways, recognize key events in disease development and track disease progression.

Report on the Workshop and Regular Meeting of the Imode-CKD and Bcmolmed Marie Curie Training and Research Programs.

A Workshop and Regular Meeting of the Marie Curie Training and Research Programs iMODECKD (Identification of the Molecular Determinants of established Chronic Kidney Disease) and BCMolMed (Molecular Medicine for Bladder Cancer) was held from 20-22 March at the Macedonian Academy of Science and Arts (MASA). The meeting was hosted by the participating center University of Skopje (SKO) - Goce Spasovski and MASA - Momir Polenakovic (R. Macedonia). The representative from MASA proteomic research center - Katerina Davalieva (R. Macedonia) had presentation on proteomic research in prostate cancer (PCa). 40 researchers from 13 different countries participated at the meeting. The Workshop was devoted on "Chronic Kidney Disease: Clinical Management issues", and consisted of 15 oral presentations given by nephrologists and experts in the field of CKD. Raymond Vanholder (Belgium) - past president of ERA-EDTA had a keynote lecture on "CKD: Questions that need to be answered and are not (or at least not entirely)". The workshop continued in four sessions with lectures from Alberto Ortiz (Spain), Olivera Stojceva-Taneva (R. Macedonia), Dimitrios Goumenos (Greece), Joachim Beige (Germany), Marian Klinger (Poland), Goce Spasovski (R. Macedonia), Joachim Jankowski (Germany), Adalbert Schiller (Romania), Robert Johnson (USA), Franco Ferrario (Italy), Ivan Rychlik (Czech Republic), Fulvio Magni (Italy) and Giovambattista Capasso (Italy), all covering a training theme. Within the meeting there were two lectures on complimentary skills for ethics in science and career advancement from two principal investigators - Goce Spasovski (R. Macedonia) and Joost Schanstra (France). During the Regular Meeting, 13 PhD students i.e. Early Stage Researchers and one Experienced Researcher from both Programs presented their work and progress within iMODE-CKD and BCMolMed projects. This meeting was a great opportunity to exchange experience and ideas in the field of systems biology approaches and translational medicine and planning future collaboration.

Structure and mechanism of action of tau aggregation inhibitors.

Since the discovery of phenothiazines as tau protein aggregation inhibitors, many additional small molecule inhibitors of diverse chemotype have been discovered and characterized in biological model systems. Although direct inhibition of tau aggregation has shown promise as a potential treatment strategy for depressing neurofibrillary lesion formation in Alzheimer's disease, the mechanism of action of these compounds has been unclear. However, recent studies have found that tau aggregation antagonists exert their effects through both covalent and non-covalent means, and have identified associated potency and selectivity driving features. Here we review small-molecule tau aggregation inhibitors with a focus on compound structure and inhibitory mechanism. The elucidation of inhibitory mechanism has implications for maximizing on-target efficacy while minimizing off-target side effects.

Structural determinants of Tau aggregation inhibitor potency.

Small-molecule Tau aggregation inhibitors are under investigation as potential therapeutic agents against Alzheimer disease. Many such inhibitors have been identified in vitro, but their potency-driving features, and their molecular targets in the Tau aggregation pathway, have resisted identification. Previously we proposed ligand polarizability, a measure of electron delocalization, as a candidate descriptor of inhibitor potency. Here we tested this hypothesis by correlating the ground state polarizabilities of cyanine, phenothiazine, and arylmethine derivatives calculated using ab initio quantum methods with inhibitory potency values determined in the presence of octadecyl sulfate inducer under reducing conditions. A series of rhodanine analogs was analyzed as well using potency values disclosed in the literature. Results showed that polarizability and inhibitory potency directly correlated within all four series. To identify putative binding targets, representative members of the four chemotypes were added to aggregation reactions, where they were found to stabilize soluble, but SDS-resistant Tau species at the expense of filamentous aggregates. Using SDS resistance as a secondary assay, and a library of Tau deletion and missense mutants as targets, interaction with cyanine was localized to the microtubule binding repeat region. Moreover, the SDS-resistant phenotype was completely dependent on the presence of octadecyl sulfate inducer, but not intact PHF6/PH6* hexapeptide motifs, indicating that cyanine interacted with a species in the aggregation pathway prior to nucleus formation. Together the data suggest that flat, highly polarizable ligands inhibit Tau aggregation by interacting with folded species in the aggregation pathway and driving their assembly into soluble but highly stable Tau oligomers.

Ligand electronic properties modulate tau filament binding site density.

Small molecules that bind tau-bearing neurofibrillary lesions are being sought for premortem diagnosis, staging, and treatment of Alzheimer's disease and other tauopathic neurodegenerative diseases. The utility of these agents will depend on both their binding affinity and binding site density (B(max)). Previously we identified polarizability as a descriptor of protein aggregate binding affinity. To examine its contribution to binding site density, we investigated the ability of two closely related benzothiazole derivatives ((E)-2-[[4-(dimethylamino)phenyl]azo]-6-methoxybenzothiazole) and ((E)-2-[2-[4-(dimethylamino)phenyl]ethenyl]-6-methoxybenzothiazole) that differed in polarizability to displace probes of high (Thioflavin S) and low (radiolabeled (E,E)-1-iodo-2,5-bis(3-hydroxycarbonyl-4-methoxy)styrylbenzene; IMSB) density sites. Consistent with their site densities, Thioflavin S completely displaced radiolabeled IMSB, but IMSB was incapable of displacing Thioflavin S. Although both benzothiazoles displaced the low B(max) IMSB probe, only the highly polarizable analog displaced near saturating concentrations of the Thioflavin S probe. Quantum calculations showed that high polarizability reflected extensive pi-electron delocalization fostered by the presence of electron donating and accepting groups. These data suggest that electron delocalization promotes ligand binding at a subset of sites on tau aggregates that are present at high density, and that optimizing this aspect of ligand structure can yield tau-directed agents with superior diagnostic and therapeutic performance.

QSAR studies for prediction of cross-ß sheet aggregate binding affinity and selectivity.

Protein aggregates that accumulate in neurodegenerative diseases are important targets of radiotracer discovery efforts. Although multiple scaffold classes have been reported to bind cross-ß sheet structure, their mechanism of binding and their ability to interact selectively with aggregates of varying protein composition are not well understood. Here we take a ligand-based quantitative structure-activity relationship approach to identify descriptors of binding affinity and selectivity for a series of 50 closely related benzothiazole derivatives reported to displace Thioflavin T fluorescent probe from synthetic aggregates composed of ß-amyloid peptide and insulin. Using a two-step workflow involving both partial least squares and multiple linear regression methods, compound polarizability and hydrophobicity were identified as tunable mediators of binding selectivity. The correlations also revealed how polarizability could be modulated in neutral compounds having push-pull character. These data suggest that the relative affinity of small molecules for binding sites exposed on aggregate surfaces can be modulated by simple chemical design considerations that are compatible with multiple scaffolds.

Research towards tau imaging.

Tau-bearing neurofibrillary lesions present a promising biomarker for premortem diagnosis and staging of Alzheimer's disease and certain forms of frontotemporal lobar degeneration by whole brain imaging methods. Although brain penetrating compounds capable of binding tau aggregates with high affinity have been disclosed for this purpose, the major barrier to progress remains the need for tau lesion binding selectivity relative to amyloid-beta plaques and other deposits of proteins in cross-beta-sheet conformation. Here we discuss challenges faced in the development of tau lesion-selective imaging agents, and recent preclinical advances in pursuit of this goal.

Ligand polarizability contributes to tau fibril binding affinity.

Whole brain imaging of tau-bearing neurofibrillary lesions has the potential to improve the premortem diagnosis and staging of Alzheimer's disease. Diverse compounds with high affinity for tau aggregates have been reported from high-throughput screens, but the affinity driving features common among them have not been determined. To identify these features, analogs of compounds discovered by high-throughput screening, including phenothiazine, triarylmethine, benzothiazole, and oxindole derivatives, were tested for their ability to displace fluorescent thioflavin dyes from filaments made from recombinant tau protein or authentic paired helical filaments purified from Alzheimer's disease tissue. When representative members of all scaffolds were assayed, the rank order of binding affinity determined for synthetic and authentic filaments correlated strongly, indicating that synthetic filaments have predictive utility for ligand development. Within individual scaffold families, binding affinity was found to correlate with compound polarizability, consistent with a role for dispersion forces in mediating ligand binding. Overall, the data indicate that polarizability is an important commonality among structurally diverse tau binding ligands, and that affinity for tau aggregates can be maximized by integrating formal assessment of this parameter into ligand discovery efforts.