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- Hämäläinen, Markku D., et al.
(författare)
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Characterization of a set of HIV-1 protease inhibitors using binding kinetics data from a biosensor-based screen
- 2000
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Ingår i: Journal of Biomolecular Screening. - : Elsevier BV. - 1087-0571 .- 1552-454X. ; 5:5, s. 353-359
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Tidskriftsartikel (refereegranskat)abstract
- The interaction between 290 structurally diverse human immunodeficiency virus type 1 (HIV-1) protease inhibitors and the immobilized enzyme was analyzed with an optical biosensor, Although only a single concentration of inhibitor was used, information about the kinetics of the interaction could be obtained by extracting binding signals at discrete time points. The statistical correlation between the biosensor binding data, inhibition of enzyme activity (K-i), and viral replication (EC50) revealed that the association and dissociation rates for the interaction could be resolved and that they were characteristic for the compounds. The most potent inhibitors, with respect to K-i and EC50 values, including the clinically used drugs, all exhibited fast association and slow dissociation rates. Selective or partially selective binders for HIV-1 protease could be distinguished from compounds that showed a general protein-binding tendency by using three reference target proteins. This biosensor-based direct binding assay revealed a capacity to efficiently provide high-resolution information on the interaction kinetics and specificity of the interaction of a set of compounds with several targets simultaneously.
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| 2. |
- Wallden, Mats, et al.
(författare)
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Evaluation of 6 years of eHealth data in the alcohol use disorder field indicates improved efficacy of care
- 2024
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Ingår i: Frontiers in Digital Health. - : Frontiers Media S.A.. - 2673-253X. ; 5
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundPredictive eHealth tools will change the field of medicine, however long-term data is scarce. Here, we report findings on data collected over 6 years with an AI-based eHealth system for supporting the treatment of alcohol use disorder.MethodsSince the deployment of Previct Alcohol, structured data has been archived in a data warehouse, currently comprising 505,641 patient days. The frequencies of relapse and caregiver-patient messaging over time was studied. The effects of both introducing an AI-driven relapse prediction tool and the COVID-19 pandemic were analyzed.ResultsThe relapse frequency per patient day among Previct Alcohol users was 0.28 in 2016, 0.22 in 2020 and 0.25 in 2022 with no drastic change during COVID-19. When a relapse was predicted, the actual occurrence of relapse in the days immediately after was found to be above average. Additionally, there was a noticeable increase in caregiver interactions following these predictions. When caregivers were not informed of these predictions, the risk of relapse was found to be higher compared to when the prediction tool was actively being used. The prediction tool decreased the relapse risk by 9% for relapses that were of short duration and by 18% for relapses that lasted more than 3 days.ConclusionsThe eHealth system Previct Alcohol allows for high resolution measurements, enabling precise identifications of relapse patterns and follow up on individual and population-based alcohol use disorder treatment. eHealth relapse prediction aids the caregiver to act timely, which reduces, delays, and shortens relapses.
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| 3. |
- Zetterström, Andreas, et al.
(författare)
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Maximum Time Between Tests : A Digital Biomarker to Detect Therapy Compliance and Assess Schedule Quality in Measurement-Based eHealth Systems for Alcohol Use Disorder
- 2019
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Ingår i: Alcohol and Alcoholism. - : OXFORD UNIV PRESS. - 0735-0414 .- 1464-3502. ; 54:1, s. 70-72
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Tidskriftsartikel (refereegranskat)abstract
- Aim: To evaluate, in a breathalyzer-based eHealth system, whether the time-based digital biomarker maximum time between tests' (MTBT) brings valuable information on alcohol consumption patterns as confirmed by correlation with blood phosphatidyl ethanol (PEth), serum carbohydrate deficient transferrin (CDT) and timeline follow-back data.Method: Data on 54 patients in follow-up for treatment of alcohol use disorder were analysed.Results: The model of weekly averages of 24-log transformed MTBT adequately described timeline follow-back data (P < 0.0001, R = 0.27-0.38, n = 650). Significant correlations were noted between MTBT and PEth (P < 0.0001, R = 0.41, n = 148) and between MTBT and CDT (P < 0.0079, R = 0.22, n = 120).Conclusions: The time-based digital biomarker maximum time between tests' described here has the potential to become a generally useful metric for all scheduled measurement-based eHealth systems to monitor test behaviour and compliance, factors important for dosing' of eHealth systems and for early prediction and interventions of lapse/relapse.
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| 4. |
- Zetterström, Andreas, et al.
(författare)
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Processing incomplete questionnaire data into continuous digital biomarkers for addiction monitoring
- 2022
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 17:7
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: eHealth systems allow efficient daily smartphone-based collection of self-reported data on mood, wellbeing, routines, and motivation; however, missing data is frequent. Within addictive disorders, missing data may reflect lack of motivation to stay sober. We hypothesize that qualitative questionnaire data contains valuable information, which after proper handling of missing data becomes more useful for practitioners.Methods: Anonymized data from daily questionnaires containing 11 questions was collected with an eHealth system for 751 patients with alcohol use disorder (AUD). Two digital continuous biomarkers were composed from 9 wellbeing questions (WeBe-i) and from two questions representing motivation/self-confidence to remain sober (MotSC-i). To investigate possible loss of information in the process of composing the digital biomarkers, performance of neural networks to predict exacerbation events (relapse) in alcohol use disorder was compared.Results: Long short-term memory (LSTM) neural networks predicted a coming exacerbation event 1-3 days (AUC 0.68-0.70) and 5-7 days (AUC 0.65-0.68) in advance on unseen patients. The predictive capability of digital biomarkers and raw questionnaire data was equal, indicating no loss of information. The transformation into digital biomarkers enable a continuous graphical display of each patient's clinical course and a combined interpretation of qualitative and quantitative aspects of recovery on a time scale.Conclusion: By transforming questionnaire data with large proportion of missing data into continuous digital biomarkers, the information captured by questionnaires can be more easily used in clinical practice. Information, assessed by the capability to predict exacerbation events of AUD, is preserved when processing raw questionnaire data into digital biomarkers.
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| 5. |
- Zetterström, Andreas, et al.
(författare)
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The Clinical Course of Alcohol Use Disorder Depicted by Digital Biomarkers
- 2021
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Ingår i: Frontiers in Digital Health. - : Frontiers Media S.A.. - 2673-253X. ; 3
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Tidskriftsartikel (refereegranskat)abstract
- Aims: This study introduces new digital biomarkers to be used as precise, objective tools to measure and describe the clinical course of patients with alcohol use disorder (AUD).Methods: An algorithm is outlined for the calculation of a new digital biomarker, the recovery and exacerbation index (REI), which describes the current trend in a patient's clinical course of AUD. A threshold applied to the REI identifies the starting point and the length of an exacerbation event (EE). The disease patterns and periodicity are described by the number, length, and distance between EEs. The algorithms were tested on data from patients from previous clinical trials (n = 51) and clinical practice (n = 1,717).Results: Our study indicates that the digital biomarker-based description of the clinical course of AUD might be superior to the traditional self-reported relapse/remission concept and conventional biomarkers due to higher data quality (alcohol measured) and time resolution. We found that EEs and the REI introduce distinct tools to identify qualitative and quantitative differences in drinking patterns (drinks per drinking day, phosphatidyl ethanol levels, weekday and holiday patterns) and effect of treatment time.Conclusions: This study indicates that the disease state-level, trend and periodicity-can be mathematically described and visualized with digital biomarkers, thereby improving knowledge about the clinical course of AUD and enabling clinical decision-making and adaptive care. The algorithms provide a basis for machine-learning-driven research that might also be applied for other disorders where daily data are available from digital health systems.
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