The DOI matches its Crossref record; 75 of the 75 references it cites were found in a registry or an index.
Drug–Target Kinetics in Drug Discovery
Open accessAbstract
The development of therapies for the treatment of neurological cancer faces a number of major challenges including the synthesis of small molecule agents that can penetrate the blood-brain barrier (BBB). Given the likelihood that in many cases drug exposure will be lower in the CNS than in systemic circulation, it follows that strategies should be employed that can sustain target engagement at low drug concentration. Time dependent target occupancy is a function of both the drug and target concentration as well as the thermodynamic and kinetic parameters that describe the binding reaction coordinate, and sustained target occupancy can be achieved through structural modifications that increase target (re)binding and/or that decrease the rate of drug dissociation. The discovery and deployment of compounds with optimized kinetic effects requires information on the structure-kinetic relationships that modulate the kinetics of binding, and the molecular factors that control the translation of drug-target kinetics to time-dependent drug activity in the disease state. This Review first introduces the potential benefits of drug-target kinetics, such as the ability to delineate both thermodynamic and kinetic selectivity, and then describes factors, such as target vulnerability, that impact the utility of kinetic selectivity. The Review concludes with a description of a mechanistic PK/PD model that integrates drug-target kinetics into predictions of drug activity.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The resurgence of covalent drugs
Singh, J., Petter, R. C., Baillie, T. A., & Whitty, A. (2011) · Nature Reviews Drug Discovery · doi:10.1038/nrd3410 · Cited by 1,873
Honigberg, L. A., Smith, A. M., Sirisawad, M., Verner, E., Loury, D., Chang, B., Li, S., Pan, Z., Thamm, D. H., Miller, R. A., et al. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1004594107 · Cited by 1,561
Drug-target residence time and its implications for lead optimization
Copeland, R. A., Pompliano, D. L., & Meek, T. D. (2006) · Nature Reviews Drug Discovery · doi:10.1038/nrd2082 · Cited by 1,464
A Unique Structure for Epidermal Growth Factor Receptor Bound to GW572016 (Lapatinib)
Wood, E. R., Truesdale, A. T., McDonald, O. B., Yuan, D., Hassell, A., Dickerson, S. H., Ellis, B., Pennisi, C., Horne, E., Lackey, K., et al. (2004) · Cancer Research · doi:10.1158/0008-5472.can-04-1168 · Cited by 1,122
The Behavior and Significance of Slow‐Binding Enzyme Inhibitors
Morrison, J. F., & Walsh, C. T. (1988) · Advances in Enzymology - and Related Areas of Molecular Biology · doi:10.1002/9780470123072.ch5 · Cited by 840
Clinical biomarkers in drug discovery and development
Frank, R., & Hargreaves, R. (2003) · Nature Reviews Drug Discovery · doi:10.1038/nrd1130 · Cited by 794
The drug–target residence time model: a 10-year retrospective
Copeland, R. A. (2016) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2015.18 · Cited by 722
Global Kinetic Explorer: A new computer program for dynamic simulation and fitting of kinetic data
Johnson, K. A., Simpson, Z. B., & Blom, T. (2009) · Analytical Biochemistry · doi:10.1016/j.ab.2008.12.024 · Cited by 595
Meyer, J. H., Wilson, A. A., Sagrati, S., Hussey, D., Carella, A., Potter, W. Z., Ginovart, N., Spencer, E. P., Cheok, A., & Houle, S. (2004) · American Journal of Psychiatry · doi:10.1176/appi.ajp.161.5.826 · Cited by 517
NanoBRET—A Novel BRET Platform for the Analysis of Protein–Protein Interactions
Machleidt, T., Woodroofe, C. C., Schwinn, M. K., Méndez, J., Robers, M. B., Zimmerman, K., Otto, P., Daniels, D. L., Kirkland, T. A., & Wood, K. V. (2015) · ACS Chemical Biology · doi:10.1021/acschembio.5b00143 · Cited by 491
Cited by
Why 90% of clinical drug development fails and how to improve it?
Sun, D., Gao, W., Hu, H., & Zhou, S. (2022) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2022.02.002 · Cited by 1,763
Challenges and Opportunities in Cancer Drug Resistance
Ward, R. A., Fawell, S., Floc’h, N., Flemington, V., McKerrecher, D., & Smith, P. D. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00383 · Cited by 445
Genome-wide gene expression tuning reveals diverse vulnerabilities of M. tuberculosis
Bosch, B., DeJesus, M. A., Poulton, N. C., Zhang, W., Engelhart, C. A., Zaveri, A., Lavalette, S., Ruecker, N., Trujillo, C., Wallach, J. B., et al. (2021) · Cell · doi:10.1016/j.cell.2021.06.033 · Cited by 316
Tang, Z., Chen, S.-H., & Chang, C.-E. A. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b01153 · Cited by 28
How Robust Is the Ligand Binding Transition State?
Bose, S., Lotz, S. D., Deb, I., Shuck, M., Lee, K. S. S., & Dickson, A. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.3c08940 · Cited by 16
Metadata
- Authors
- Tonge, P. J.
- Venue
- ACS Chemical Neuroscience
- Publisher
- American Chemical Society (ACS)
- Published
- 2017-07-14
- Volume
- 9
- Issue
- 1
- Pages
- 29-39
- ISSN
- 1948-7193 (ISSN-L)
- OpenAlex
- W2664285537
- Cited by
- 287
- References
- 75
- FWCI
- 17.35
- Type
- journal article
- Language
- en
- Open access
- hybrid
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Drug–Target Kinetics in Drug Discovery. https://app.cochat.ai/papers/10.1021/acschemneuro.7b00185
- The paper
- Tonge, P. J. (2018). Drug–Target Kinetics in Drug Discovery. ACS Chemical Neuroscience. https://doi.org/10.1021/acschemneuro.7b00185
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.