ArticlePloS one2026
Machine learning-based unified models for predicting drug clearance from pharmacokinetic animal and study design variables.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Clearance (CL) is a primary pharmacokinetic (PK) parameter crucial to determine how quickly a drug is eliminated from the body, which guides the appropriate dosing interval to maintain a consistent concentration in blood. Given the importance of CL, this study aimed to use machine learning (ML) techniques to predict CL values by identifying patterns and relationships within an extracted dataset of PK variables from published articles. Variables evaluated in the extracted dataset included drug, dose, animal species, and route of administration. Nine distinct ML models were then applied to analyze the CL data, incorporating both imbalanced and balanced data generated through resampling methods. Since the CL data used in this study is a collection of all CL values (true CL and CL/F) extracted from scientific articles, the collected CL variable for both IV and non-IV administration routes are referred to as hybrid ML CL. To analyze the effect of ML models in predicting the CL values, we used the hybrid ML CL dataset for six different subsets of data including one solely from the intravenous route of administration. Linear regression, multi-layer perceptron, and random forest models consistently had the highest efficiency in predicting CL values, with an R2 score > 0.87. However, R2 increased to > 0.95 when analyzing only ungulates or small ruminants, and > 0.92 for the companion animal group. This study has the potential to help researchers employ computational, mathematical, and ML models to predict and estimate CL values and changes in CL values based on variables. This study focuses on evaluating the feasibility of predicting drug CL in situations where direct CL data are not available. Rather than addressing drug development processes, the research examines whether study design variables can serve as input parameters for a proposed cross-species extrapolation tool aimed specifically at predicting existing drug CL values.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.