Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 5 appraisals
Journal of pharmacokinetics and pharmacodynamics
Multi-objective optimization in population pharmacokinetic model selection and optimization: application of NSGA-II in pyDarwin
The selection of a "good" model usually involves a combination of objective and subjective criteria. Although many aspects of model quality can be expressed numerically, certain desirable characteristics remain difficult-or even impossible-to quantify precisely. Multi-objective optimization (MOO) provides a systematic way to handle this challenge by explicitly incorporating and balancing both objective (measurable) and subjective (judgment-based) considerations when choosing among candidate solutions. The generated Pareto front represents a set of non-dominated models where no single solution can be improved in one objective without sacrificing the performance in another objective. Using the non-dominated sorting genetic algorithm II (NSGA-II), an implementation of MOO, we simultaneously considered objective function value and number of estimated parameters as competing criteria. Concentration measurements of 17-DMAG, quetiapine, clozapine and ziprasidone were applied to build population pharmacokinetic models through traditional stepwise search, machine learning based single-objective hybrid genetic algorithm (SOHGA) and MOO. Local downhill search with MOO was also assessed in this study. While both objectives improved, models with lower objective function value generally contained more estimated parameters. The number of non-dominated solutions for DMAG, ziprasidone, clozapine, and quetiapine was 17, 9, 9, and 13, respectively. The optimal model selected by SOHGA appeared on the Pareto front for DMAG, ziprasidone and clozapine datasets. Overall, MOO provides objective transparency to the cost of tradeoffs between competing model objectives, allowing researchers to better contextualize subjective criteria (e.g., biological plausibility, improvements in diagnostic plots) when aligning model selection with clinical context.
18 May 2026
Read appraisal →Journal of computer-aided molecular design
Dynamic stability-driven machine learning improves binding pose identification on human serum albumin.
Accurate identification of ligand binding modes on human serum albumin (HSA) is important for understanding drug-protein interactions that influence pharmacokinetics, including site-specific competition and protein-binding displacement. However, because HSA is a large, multisite binding protein that permits diverse binding configurations, conventional molecular docking often struggles to distinguish crystal-like poses from nonspecific surface-binding poses using static scoring functions alone. Here, we developed a machine-learning framework for HSA binding-pose identification that incorporates dynamic stability information derived from short molecular dynamics (MD) simulations. Time-dependent interaction patterns observed during MD trajectories, including contact persistence, distance fluctuations, and interaction energy-related descriptors, were quantified as interpretable features and integrated with docking scores. Using Leave-One-PDB-Out cross-validation on 31 HSA-ligand crystal structures, models incorporating MD-derived features significantly outperformed the docking score-only model under both pose-level (root-mean-square deviation [RMSD] ≤ 2.5 Å) and site-level (RMSD ≤ 5.0 Å) criteria (Holm-corrected p < 0.001). External validation using a temporally independent nateglinide-HSA complex further supported the utility of the framework for discriminating plausible binding modes within the HSA setting. Notably, informative dynamic features were obtained from 5 ns MD simulations, indicating that early-stage dynamic behavior is sufficient to improve pose discrimination. These findings show that short MD simulations can provide practically useful and interpretable features for machine-learning-based binding-pose identification on HSA. While broader validation will be required to establish applicability beyond HSA, the proposed framework offers a practical strategy for improving binding-mode analysis in pharmacokinetically relevant HSA studies.
15 May 2026
Read appraisal →Journal of medicinal chemistry
An Interpretable, Thermodynamics-Based Deep Learning Framework for Predicting and Optimizing Drug Membrane Permeability
Cellular membranes serve as selective barriers, and membrane permeability is crucial for drug pharmacokinetics. While in vitro and in vivo methods exist, predicting and designing membrane permeability remains challenging. We developed a thermodynamics-based deep learning framework to analyze the structure-permeability relationship, based on the concept that interactions between membrane lipids and small molecules influence permeability. We determined the membrane penetration thermodynamics of 8,239 compounds using coarse-grained molecular dynamics simulations and created interpretable graph neural network models to predict and design drug membrane permeability. As a proof-of-concept, we designed a novel nasal-administered melatonin analog, MT-A2, optimized for permeability. Compared with melatonin, MT-A2 showed superior nasal absorption, prolonged brain retention, and enhanced sleep efficacy. Our results provide a promising approach for predicting and designing membrane permeability, aiding in the development of drugs with better pharmacokinetics.
25 Apr 2026
Read appraisal →International journal of pharmaceutics
Optimization of drug diffusion in drug-eluting stents for coronary artery based on deep reinforcement learning
Drug-eluting stents (DES) are extensively used to treat coronary artery disease, and improving their therapeutic efficacy remains a long-standing research objective. Existing optimization strategies for DES drug delivery lack adaptive, learning-based frameworks capable of simultaneously enhancing drug concentration and uniformity in the therapeutic domain while regulating levels in the non-treatment domain, and show limited capacity to address complex, nonlinear optimization problems. This study applies a deep reinforcement learning (DRL)-based framework to optimize drug diffusion in DES. By integrating neural networks with the proximal policy optimization (PPO) algorithm, the framework enables closed-loop, feedback-driven regulation of drug diffusion in real time and demonstrates robustness to stochastic disturbances and parametric variations. The method was systematically evaluated across three stent embedding configurations: half-embedded, fully embedded, and non-embedded. Results show the DRL agent successfully balances the three therapeutic objectives and adopts distinct control strategies for different configurations. All configurations exhibited improved overall performance. For the half-embedded configuration, the DRL policy increased therapeutic domain concentration by 6.99%, accompanied by a slight rise in the non-treatment domain and a minor decrease in uniformity within the therapeutic domain. Under the fully embedded configuration, concentrations in both domains remained essentially unchanged, whereas uniformity in the therapeutic domain improved by 8.14%. For the non-embedded configuration, both therapeutic domain concentration and uniformity increased (by 2.04% and 6.30%, respectively), with negligible change in the non-treatment domain. These results demonstrate the feasibility of DRL for adaptive DES optimization and highlight its potential for patient-specific applications and guidance in clinical selection of drug-embedding configurations.
12 Apr 2026
Read appraisal →Pharmacology research & perspectives
Improving Population Pharmacokinetic Modelling with Artificial Patients using Generative Artificial Intelligence
In population pharmacokinetics (PopPK), non-linear mixed effects (NLME) models are used to simultaneously describe a drug's pharmacokinetics (PK) and dynamics (PD) in a patient population using systems of ordinary differential equations. In this field, machine learning is mainly used for data preparation, hypothesis generation, predictive modeling, and model validation. Some approaches to integrate artificially generated information have already been explored, but real-world application is still limited. We therefore conducted a proof-of-concept study to analyze the ability of generative artificial intelligence (AI) to create artificial patient profiles to augment PopPK data sets and assess their influence on parameter estimates. We defined the pharmacokinetic parameters of a hypothetical drug and simulated the concentration curves of 20 patients. We then trained Wasserstein Generative Adversarial Networks (WGANs) with gradient penalty (GP) to generate artificial patients. The data distribution of original and artificial patients was statistically indistinguishable as shown by the Maximum Mean Discrepancy test. Therefore, the WGAN-GP is neither overfitted, that is producing only single instances of artificial patients, nor underfitted, that is producing unrealistic artificial patients. We then combined different shares of original and artificial patients in separate data sets to build and compare PopPK model estimates. Addition of artificial patients led to narrower confidence intervals, indicating more robust parameter estimates, and accentuated the allometric effect of weight on the volume of distribution. In conclusion, we provide a proof-of-concept that generative AI can be used to augment pharmacokinetic data sets, with preliminary evidence suggesting improved parameter estimation.
4 Apr 2026
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