The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Computer systems for the prediction of xenobiotic metabolism
Abstract
The aim of pharmaceutical research and development is to ensure a continuing pipeline of new chemical entities (NCEs) displaying high therapeutic efficacy with few or no side effects. Failure of promising lead candidates late in the drug discovery processes is regarded as commercially unacceptable in today's increasingly competitive business environment. An inappropriate ADME/Toxicity profile in humans is the major cause of failure of lead candidates in late clinical stages of drug development. Combinatorial chemistry techniques coupled with high throughput screening protocols means that pharmaceutical companies are now dealing with an unprecedented number of NCEs on an annual basis. As a consequence, screening for undesirable ADME/Toxicity properties in the early stages of drug development, preferably pre-synthesis, is now considered the essential paradigm. In silico assessment of NCEs is rapidly emerging as the next wave of technology for early ADME/Toxicity prediction. In this review, we discuss the major commercially available products for the assessing the potential metabolic activity of xenobiotic substances in mammalian systems.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“In fact, there are .300different empirical rules that allow one to predict the fate of metabolites on the basis of their chemical structure (69).”
From The food metabolome: a window over dietary exposure (2014) · cites this paper as [69]
Citations
Cites
Weininger, D. (1988) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00057a005 · Cited by 7,436
Drug Metabolism: Chemical and Biochemical Aspects
Testa, B., & Jenner, P. G. (1976) · Medical Entomology and Zoology · Cited by 308
Knowledge-Based Expert Systems for Toxicity and Metabolism Prediction: DEREK, StAR and METEOR
Greene, N., Judson, P. N., Langowski, J. J., & Marchant, C. A. (1999) · SAR and QSAR in environmental research · doi:10.1080/10629369908039182 · Cited by 251
Self-citationQSAR in Environmental Toxicology - II
(1987) · Springer eBooks · doi:10.1007/978-94-009-3937-0 · Cited by 248
Ekins, S., de Groot, M. J., & Jones, J. P. (2001) · PubMed · Cited by 202
META. 1. A Program for the Evaluation of Metabolic Transformation of Chemicals
Klopman, G., Dimayuga, M., & Talafous, J. (1994) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00022a014 · Cited by 170
Smith, D. A., Ackland, M. J., & Jones, B. (1997) · Drug Discovery Today · doi:10.1016/s1359-6446(97)01085-4 · Cited by 121
META. 2. A Dictionary Model of Mammalian Xenobiotic Metabolism
Talafous, J., Sayre, L. M., Mieyal, J. J., & Klopman, G. (1994) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00022a015 · Cited by 99
Properties of cytochrome P450 isoenzymes and their substrates Part 1: active site characteristics
Smith, D. A., Ackland, M. J., & Jones, B. (1997) · Drug Discovery Today · doi:10.1016/s1359-6446(97)01081-7 · Cited by 91
Quantitative Structure-Activity Relationships of Cytochrome P-450
Hansch, C. H., & Zhang, L. (1993) · Drug Metabolism Reviews · doi:10.3109/03602539308993972 · Cited by 75
Research works printed but not linked: 1
- 1988, Vol. 1–7
Cited by
Sorted by citations
ADMET in silico modelling: towards prediction paradise?
Waterbeemd, H. V. D., & Gifford, E. (2003) · Nature Reviews Drug Discovery · doi:10.1038/nrd1032 · Cited by 1,924
In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling
Ekins, S., Mestres, J., & Testa, B. (2007) · British Journal of Pharmacology · doi:10.1038/sj.bjp.0707305 · Cited by 721
The food metabolome: a window over dietary exposure
Scalbert, A., Brennan, L., Manach, C., Andres-Lacueva, C., Dragsted, L. O., Draper, J., Rappaport, S. M., van der Hooft, J. J., & Wishart, D. S. (2014) · The American Journal of Clinical Nutrition · doi:10.3945/ajcn.113.076133 · Cited by 519
Jamei, M., Dickinson, G. L., & Rostami‐Hodjegan, A. (2009) · Drug Metabolism and Pharmacokinetics · doi:10.2133/dmpk.24.53 · Cited by 377
Marchant, C. A., Briggs, K. A., & Long, A. (2008) · Toxicology Mechanisms and Methods · doi:10.1080/15376510701857320 · Cited by 344
Self-citation
Metadata
- Authors
- Langowski, J., & Long, A.
- Affiliations
- University of Leeds (GB)
- Corresponding author
- Long, Anthony
- Venue
- Advanced Drug Delivery Reviews
- Publisher
- Elsevier BV
- Published
- 2002-03
- Volume
- 54
- Issue
- 3
- Pages
- 407-415
- ISSN
- 0169-409X (ISSN-L)
- OpenAlex
- W2044694490
- Cited by
- 138
- References
- 23
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 1 · 2023: 2 · 2022: 1 · 2021: 5 · 2020: 2 · 2019: 7 · 2018: 3 · 2017: 4 · 2016: 2 · 2015: 4 · 2014: 2 · 2013: 8 · 2012: 4
- FWCI
- 1.55
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Advanced Drug Delivery Reviews (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmacogenetics and Drug Metabolism
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Drug-Induced Hepatotoxicity and Protection
- Keywords
- new chemical entities, drug discovery, mammalian metabolism, computer-aided drug design, toxicity prediction
- MeSH terms
- Animals, Computers, Enzymes, Expert Systems, Humans, Models, Molecular, Predictive Value of Tests, Xenobiotics, Databases, Factual, Computational Biology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
