In silico prediction of volume of distribution in human using linear and nonlinear models on a 669 compound data set.
Explore this paper's citation graph
Summary
The authors' analysis generated models, both via a single method or consensus which were able to predict human VD(ss) within geometric mean 2-fold error, a predictive accuracy considered good even for more resource-intensive approaches such as those requiring data generated from studies in multiple animal species.
- Type
- article
- Published
- 2009-06-17
- Cited by
- 80
- References
- 0
- OpenAlex
- https://openalex.org/W2000613614
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:20256350
Keywords
In silico, Nonlinear system, Linear model, Data set, Applicability domain
References
No references recorded for this paper.
Cited by
- Ocular and systemic pharmacokinetic models for drug discovery and development
- Quantitative Structure - Pharmacokinetics Relationships Analysis of Basic Drugs: Volume of Distribution.
- Modeling Phospholipidosis Induction: Reliability and Warnings
- Prediction of steady-state volume of distribution of acidic drugs by quantitative structure-pharmacokinetics relationships.
- Prediction of human volume of distribution values for drugs using linear and nonlinear quantitative structure pharmacokinetic relationship models
- BDDCS Class Prediction for New Molecular Entities
- Predicting Total Clearance in Humans from Chemical Structure
- The right compound in the right assay at the right time: an integrated discovery DMPK strategy
- Dabigatran, intracranial hemorrhage, and the neurosurgeon.
- pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures
- Tissue-to-blood distribution coefficients in the rat: utility for estimation of the volume of distribution in man.
- Impact of ion class and time on oral drug molecular properties
- Enantiomeric pairs reveal that key medicinal chemistry parameters vary more than simple physical property based models can explain
- Predicting human exposure of active drug after oral prodrug administration, using a joined in vitro/in silico-in vivo extrapolation and physiologically-based pharmacokinetic modeling approach.
- Molecular Interaction Fields (MIFs) to Predict Lipophilicity and ADME Profile of Antitumor Pt(II) Complexes
- Pharmaceutical Perspectives of Nonlinear QSAR Strategies
- In silico Prediction of Total Human Plasma Clearance
- DemQSAR: predicting human volume of distribution and clearance of drugs
- Self-organizing molecular field analysis of NSAIDs: assessment of pharmacokinetic and physicochemical properties using 3D-QSPkR approach.
- Development of a consumer product ingredient database for chemical exposure screening and prioritization.
Related papers
- How important is to detect systematic error in predictions and understand statistical applicability domain of QSAR models
- Comparison of in silico tools for evaluating rat oral acute toxicity†
- Predicting skin sensitisation using a decision tree integrated testing strategy with an in silico model and in chemico/in vitro assays.
- Quantitative structure-activity relationship (QSAR) models and their applicability domain analysis on HIV-1 protease inhibitors by machine learning methods
- Quantitative structure-property relationship study of liquid vapor pressures for polychlorinated diphenyl ethers
- Development of an in silico consensus model for the prediction of the phospholipigenic potential of small molecules
- From Molecular Descriptors to Intrinsic Fish Toxicity of Chemicals: An Alternative Approach to Chemical Prioritization
- Development of an Infinite Dilution Activity Coefficient Prediction Model for Organic Solutes in Ionic Liquids with Modified Partial Equalization Orbital Electronegativity Method Derived Descriptors
- Role of physicochemical properties in the estimation of skin permeability: in vitro data assessment by Partial Least-Squares Regression