Cited by 139Computational Drug Discovery MethodsPharmacogenetics and Drug MetabolismDrug-Induced Hepatotoxicity and Protection

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Computer systems for the prediction of xenobiotic metabolism

Langowski, Jan, Long, Anthony·2002·Advanced Drug Delivery Reviews

DOI 10.1016/s0169-409x(02)00011-x

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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.

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SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules
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-citation
QSAR in Environmental Toxicology - II
(1987) · Springer eBooks · doi:10.1007/978-94-009-3937-0 · Cited by 248
Pharmacophore and three-dimensional quantitative structure activity relationship methods for modeling cytochrome p450 active sites.
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
Properties of cytochrome P450 isoenzymes and their substrates part 2: properties of cytochrome P450 substrates
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

Show all 23 references

Research works printed but not linked: 1

1988, Vol. 1–7

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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)
DOI: 10.1016/s0169-409x(02)00011-x
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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

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