Dataset Information

Joint Computational/Cell-Based Approach for Screening Inhibitors of Tau Oligomerization: A Proof-of-Concept Study.

ABSTRACT:

Background

Tau assembly produces soluble oligomers and insoluble neurofibrillary tangles, which are neurotoxic to the brain and associated with Alzheimer's and Parkinson's diseases. Therefore, preventing tau aggregation is a promising therapy for those neurodegenerative disorders.

Objective

The aim of this study was to develop a joint computational/cell-based oligomerization protocol for screening inhibitors of tau assembly.

Methods

Virtual oligomerization inhibition (VOI) experiment using molecular dynamics simulation was performed to screen potential oligomerization inhibitors of PHF6 hexapeptide. Tau seeding assay, which is directly related to the outcome of therapeutic intervention, was carried out to confirm a ligand's ability in inhibiting tau assembly formation.

Results

Our protocol was tested on two known compounds, EGCG and Blarcamesine. EGCG inhibited both the aggregation of PHF6 peptide in VOI and tau assembly in tau seeding assay, while Blarcamesine was not a good inhibitor at the two tasks. We also pointed out that good binding affinity to tau aggregates is needed, but not sufficient for a ligand to become a good inhibitor of tau oligomerization.

Conclusion

VOI goes beyond traditional computational inhibitor screening of amyloid aggregation by directly examining the inhibitory ability of a ligand to tau oligomerization. Comparing with the traditional biochemical assays, tau seeding activities in cells is a better indicator for the outcome of a therapeutic intervention. Our hybrid protocol has been successfully validated. It can effectively and efficiently identify the inhibitors of amyloid oligomerization/aggregation processes, thus, facilitate to the drug development of tau-related neurodegenerative diseases.

SUBMITTER: Man VH

PROVIDER: S-EPMC10276768 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Joint Computational/Cell-Based Approach for Screening Inhibitors of Tau Oligomerization: A Proof-of-Concept Study.

Man Viet Hoang VH Lin Da D He Xibing X Gao Jie J Wang Junmei J

Journal of Alzheimer's disease : JAD 20220101 1

<h4>Background</h4>Tau assembly produces soluble oligomers and insoluble neurofibrillary tangles, which are neurotoxic to the brain and associated with Alzheimer's and Parkinson's diseases. Therefore, preventing tau aggregation is a promising therapy for those neurodegenerative disorders.<h4>Objective</h4>The aim of this study was to develop a joint computational/cell-based oligomerization protocol for screening inhibitors of tau assembly.<h4>Methods</h4>Virtual oligomerization inhibition (VOI) ...[more]

PMID: 35848028

Similar Datasets

Project description:IntroductionBenzodiazepines are the most commonly prescribed psychotropic medications, but they may place users at risk of serious adverse effects. Developing a method to predict benzodiazepine prescriptions could assist in prevention efforts.MethodsThe present study applies machine learning methods to de-identified electronic health record data, in order to develop algorithms for predicting benzodiazepine prescription receipt (yes/no) and number of benzodiazepine prescriptions (0, 1, 2+) at a given encounter. Support-vector machine (SVM) and random forest (RF) approaches were applied to outpatient psychiatry, family medicine, and geriatric medicine data from a large academic medical center. The training sample comprised encounters taking place between January 2020 and December 2021 (N = 204,723 encounters); the testing sample comprised data from encounters taking place between January and March 2022 (N = 28,631 encounters). The following empirically-supported features were evaluated: anxiety and sleep disorders (primary anxiety diagnosis, any anxiety diagnosis, primary sleep diagnosis, any sleep diagnosis), demographic characteristics (age, gender, race), medications (opioid prescription, number of opioid prescriptions, antidepressant prescription, antipsychotic prescription), other clinical variables (mood disorder, psychotic disorder, neurocognitive disorder, prescriber specialty), and insurance status (any insurance, type of insurance). We took a step-wise approach to developing a prediction model, wherein Model 1 included only anxiety and sleep diagnoses, and each subsequent model included an additional group of features.ResultsFor predicting benzodiazepine prescription receipt (yes/no), all models showed good to excellent overall accuracy and area under the receiver operating characteristic curve (AUC) for both SVM (Accuracy = 0.868-0.883; AUC = 0.864-0.924) and RF (Accuracy = 0.860-0.887; AUC = 0.877-0.953). Overall accuracy was also high for predicting number of benzodiazepine prescriptions (0, 1, 2+) for both SVM (Accuracy = 0.861-0.877) and RF (Accuracy = 0.846-0.878).DiscussionResults suggest SVM and RF algorithms can accurately classify individuals who receive a benzodiazepine prescription and can separate patients by the number of benzodiazepine prescriptions received at a given encounter. If replicated, these predictive models could inform system-level interventions to reduce the public health burden of benzodiazepines.

Project description:BackgroundDepression during the postnatal year is prevalent in mothers (17%) and fathers (9%), and suicide is the leading cause of maternal death in this period. Lifelong costs and consequences of untreated postnatal depression (PND) are high due to impacts on infants as well as parents. We aimed to improve access to PND treatment using digital screening. We developed a smartphone app (ClinTouch DAWN-P) that allows parents to monitor their mood daily with the Edinburgh Postnatal Depression Scale (EPDS), uploading responses in real-time to a secure server. We evaluated the app's feasibility, acceptability, validity and safety in a proof-of-concept study.MethodsPregnant women (≥ 36 weeks gestation) and partners were recruited from antenatal services and invited to complete daily EPDS assessments via the ClinTouch DAWN-P app until 6 weeks postpartum. Participants completed standard paper-based EPDS at two time points for validity comparisons. We examined app acceptability and usability at 6 weeks postpartum with qualitative interviews, examined using framework analysis, and the abridged Mobile App Rating Scale (convergent mixed methods design).ResultsMost (96%) eligible pregnant women approached were keen to try the app. Participating mothers (n = 15) and partners/fathers (n = 8) found the app easy to use, and 91% continued to use it for the full study period. Overall, 67% of daily app-based assessments were completed, with a history of depression predicting lower app usage. Participants suggested modifications to the app and its deployment to improve usability (e.g., extending the response window and including feedback and parenting advice). The validity of app-based responses was confirmed by high agreement with standard EPDS. App-based and paper-based ratings showed perfect agreement in identifying cases of likely PND. There were no serious adverse events relating to app use.ConclusionsDigital PND screening appears feasible, acceptable, valid and safe. It also benefits from being remotely delivered: we enrolled all participants remotely during the first COVID-19 lockdown. Use of digital screening could address known shortcomings of conventional health visitor-delivered screening such as limited staff time, parental unwillingness to disclose difficulties to a professional, lack of partner/father screening, and language barriers.Trial registrationThe study was prospectively registered (Clinicaltrials.gov: NCT04279093 ).

Dataset Information

Joint Computational/Cell-Based Approach for Screening Inhibitors of Tau Oligomerization: A Proof-of-Concept Study.

Background

Objective

Methods

Results

Conclusion

Publications

Joint Computational/Cell-Based Approach for Screening Inhibitors of Tau Oligomerization: A Proof-of-Concept Study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets