ArticleMolecular diversity2022
Construction of a prediction model for drug removal rate in hemodialysis based on chemical structures.
Article in Molecular diversity, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 4 citations in OpenAlex.
- Translational Prediction of Hemodialysis Clearance Integrating Molecular Structure and Dialysis Conditions.Clinical and translational science · 2026Article
- Onco-nephrology in clinical practice: pharmacokinetics, monitoring, and treatment strategies for patients with cancer and impaired renal function.International journal of clinical oncology · 2025Review
- Development of a Medication-Related Osteonecrosis of the Jaw Prediction Model Using the FDA Adverse Event Reporting System Database and Machine Learning.Pharmaceuticals (Basel, Switzerland) · 2025Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In designing drug dosing for hemodialysis patients, the removal rate (RR) of the drug by hemodialysis is important. However, acquiring the RR is difficult, and there is a need for an estimation method that can be used in clinical settings. In this study, the RR predictive model was constructed using the RR of known drugs by quantitative structure-activity relationship (QSAR) analysis. Drugs were divided into a model construction drug set (75%) and a model validation drug set (25%). The RR was collected from 143 medicines. The objective variable (RR) and chemical structural characteristics (descriptors) of the drug (explanatory variable) were used to construct a prediction model using partial least squares (PLS) regression and artificial neural network (ANN) analyses. The determination coefficients in the PLS and ANN methods were 0.586 and 0.721 for the model validation drug set, respectively. QSAR analysis successfully constructed dialysis RR prediction models that were comparable or superior to those using pharmacokinetic parameters. Considering that the RR dataset contains potential errors, we believe that this study has achieved the most reliable RR prediction accuracy currently available. These predictive RR models can be achieved using only the chemical structure of the drug. This model is expected to be applied at the time of hemodialysis.
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