Fusion of nonclinical and clinical data to predict human drug safety
Abstract
Adverse drug reactions continue to be a major cause of morbidity in both patients receiving therapeutics and in drug R&D programs. Predicting and possibly eliminating these adverse events remains a high priority in industry, government agencies and healthcare systems. With small molecule candidates, the fusion of nonclinical and clinical data is essential in establishing an overall system that creates a true translational science approach. Several new advances are taking place that attempt to create a 'patient context' mechanism early in drug research and development and ultimately into the marketplace. This 'life-cycle' approach has as its core the development of human-oriented, nonclinical end points and the incorporation of clinical knowledge at the drug design stage. The next 5 years should witness an explosion of what the author views as druggable and safe chemical space, pharmacosafety molecular targets and the most important aspect, an understanding of unique susceptibilities in patients developing adverse drug reactions. Our current knowledge of clinical safety relies completely on pharmacovigilance data from approved and marketed drugs, with a few exceptions of drugs failing in clinical trials. Massive data repositories now and soon to be available via cloud computing should stimulate a major effort in expanding our view of clinical drug safety and its incorporation into early drug research and development.
Integrity signals
Registry
Cites
Statistics
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?
Animal experiments offer little guidance to human toxicity reactions due to inter-species differences and differential disease models.
“the results offer little guidance to human toxicity reactions, due to inter-species differences and differential disease models [6,7].”
From Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis (2018) · cites this paper as [6]
Cites
A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer.
Paik, S., Shak, S., Tang, G., Kim, C., Baker, J., Cronin, M., Baehner, F., Walker, M. G., Watson, D., Park, T., et al. (2004) · New England Journal of Medicine · doi:10.1056/nejmoa041588 · Cited by 6,009 · see citing works
Reconstituting organ-level lung functions on a chip
Huh, D., Matthews, B. D., Mammoto, A., Montoya-Zavala, M., Hsin, H. Y., & Ingber, D. E. (2010) · Science · doi:10.1126/science.1188302 · Cited by 4,250 · see citing works
<i>SLCO1B1</i> Variants and Statin-Induced Myopathy — A Genomewide Study
(2008) · New England Journal of Medicine · doi:10.1056/nejmoa0801936 · Cited by 2,043 · see citing works
Mechanisms of adverse effects of anti-VEGF therapy for cancer
Kamba, T., & McDonald, D. M. (2007) · British Journal of Cancer · doi:10.1038/sj.bjc.6603813 · Cited by 991 · see citing works
Large-scale prediction and testing of drug activity on side-effect targets
Lounkine, E., Keiser, M. J., Whitebread, S., Mikhailov, D., Hamon, J., Jenkins, J. L., Lavan, P., Weber, E., Doak, A. K., Côté, S., et al. (2012) · Nature · doi:10.1038/nature11159 · Cited by 880 · see citing works
Molecular mechanisms of cardiotoxicity of tyrosine kinase inhibition
Force, T. L., Krause, D. S., & Etten, R. A. V. (2007) · Nature reviews. Cancer · doi:10.1038/nrc2106 · Cited by 800 · see citing works
Clinical biomarkers in drug discovery and development
Frank, R. A., & Hargreaves, R. J. (2003) · Nature Reviews Drug Discovery · doi:10.1038/nrd1130 · Cited by 794 · see citing works
Biomarkers of Acute Kidney Injury
Vaidya, V. S., Ferguson, M. A., & Bonventre, J. V. (2007) · The Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev.pharmtox.48.113006.094615 · Cited by 741 · see citing works
Tyrosine Kinase Inhibitors – A Review on Pharmacology, Metabolism and Side Effects
Hartmann, J., Haap, M., Kopp, H.-G., & Lipp, H.-P. (2009) · Current Drug Metabolism · doi:10.2174/138920009788897975 · Cited by 630 · see citing works
Possible molecular mechanisms involved in the toxicity of angiogenesis inhibition
Verheul, H. M. W., & Pinedo, H. M. (2007) · Nature reviews. Cancer · doi:10.1038/nrc2152 · Cited by 503 · see citing works
Revisiting lab-on-a-chip technology for drug discovery
Neužil, P., Giselbrecht, S., Länge, K., Huang, T. J., & Manz, A. (2012) · Nature Reviews Drug Discovery · doi:10.1038/nrd3799 · Cited by 490 · see citing works
Wysowski, D. K., & Swartz, L. (2005) · Archives of Internal Medicine · doi:10.1001/archinte.165.12.1363 · Cited by 471 · see citing works
Novel Data-Mining Methodologies for Adverse Drug Event Discovery and Analysis
Harpaz, R., DuMouchel, W., Shah, N. H., Madigan, D., Ryan, P. B., & Friedman, C. (2012) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2012.50 · Cited by 398 · see citing works
Identifying genetic risk factors for serious adverse drug reactions: current progress and challenges
Wilke, R. A., Lin, D. W., Roden, D. M., Watkins, P. B., Flockhart, D. A., Zineh, I., Giacomini, K. M., & Krauss, R. M. (2007) · Nature Reviews Drug Discovery · doi:10.1038/nrd2423 · Cited by 394 · see citing works
Trends in the Risks and Benefits to Patients With Cancer Participating in Phase 1 Clinical Trials
Roberts, T. G. (2004) · JAMA · doi:10.1001/jama.292.17.2130 · Cited by 319 · see citing works
Leaman, R., Wojtulewicz, L. B., Sullivan, R., et al. (2010) · Cited by 257 · see citing works
Thompson, R. A., Isin, E. M., Li, Y., Weidolf, L. O. G., Page, K., Wilson, I. D., Swallow, S., Middleton, B. J., Stahl, S. H., Foster, A. J., et al. (2012) · Chemical Research in Toxicology · doi:10.1021/tx300091x · Cited by 211 · see citing works
Workman, P., Aboagye, E. O., Chung, Y., Griffiths, J. R., Hart, R., Leach, M. O., Maxwell, R. J., McSheehy, P. M. J., Price, P., & Zweit, J. (2006) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djj162 · Cited by 189 · see citing works
Predicting adverse drug events from personal health messages.
Chee, B. W., Berlin, R. B., & Schatz, B. R. (2011) · PubMed · Cited by 147 · see citing works
Predicting Adverse Drug Events Using Pharmacological Network Models
Cami, A., Arnold, A. D., Manzi, S. F., & Reis, B. Y. (2011) · Science Translational Medicine · doi:10.1126/scitranslmed.3002774 · Cited by 131 · see citing works
Pharmacogenomic Biomarkers for Prediction of Severe Adverse Drug Reactions
Ingelman‐Sundberg, M. (2008) · New England Journal of Medicine · doi:10.1056/nejme0708842 · Cited by 126 · see citing works
Drug withdrawals and the lessons within.
Smith, D. A., & Schmid, E. F. (2006) · PubMed · Cited by 125 · see citing works
Bachelot, T., Ray-Coquard, I., Catimel, G., et al. (2000) · Annals of Oncology · doi:10.1023/a:1008368319526 · Cited by 119 · see citing works
Toxicity Testing in the 21st Century: A View from the Pharmaceutical Industry
MacDonald, J. S., & Robertson, R. T. (2009) · Toxicological Sciences · doi:10.1093/toxsci/kfp088 · Cited by 115 · see citing works
Pharmacogenomic Biomarkers for Prediction of Severe Adverse Drug Reactions
(2009) · SciVee · doi:10.4016/10726.01 · Cited by 104 · see citing works
Predicting Polypharmacology by Binding Site Similarity: From Kinases to the Protein Universe
Milletti, F., & Vulpetti, A. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci1001263 · Cited by 97 · see citing works
Cardiotoxicity of the new cancer therapeutics – mechanisms of, and approaches to, the problem
Force, T. L., & Kerkelä, R. (2008) · Drug Discovery Today · doi:10.1016/j.drudis.2008.05.011 · Cited by 95 · see citing works
Serum Chemical Biomarkers of Cardiac Injury for Nonclinical Safety Testing
Walker, D. B. (2006) · Toxicologic Pathology · doi:10.1080/01926230500519816 · Cited by 94 · see citing works
(Q)SAR Modeling and Safety Assessment in Regulatory Review
Kruhlak, N. L., Benz, R. D., Zhou, H., & Colatsky, T. J. (2012) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2011.300 · Cited by 92 · see citing works
Encyclopedia of Drug Metabolism and Interactions
(2011) · Wiley eBooks · doi:10.1002/9780470921920 · Cited by 68 · see citing works
Systems-ADME/Tox: Resources and network approaches
Ekins, S. (2005) · Journal of Pharmacological and Toxicological Methods · doi:10.1016/j.vascn.2005.05.005 · Cited by 65 · see citing works
Pharmacological Mechanism-Based Drug Safety Assessment and Prediction
Abernethy, D. R., Woodcock, J., & Lesko, L. (2011) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2011.55 · Cited by 64 · see citing works
Inroads to Predict in Vivo Toxicology—An Introduction to the eTOX Project
Briggs, K. A., Cases, M., Heard, D. J., et al. (2012) · International Journal of Molecular Sciences · doi:10.3390/ijms13033820 · Cited by 56 · see citing works
Pharmacogenomics and Drug Toxicity
Nakamura, Y. (2008) · New England Journal of Medicine · doi:10.1056/nejme0805136 · Cited by 55 · see citing works
Cruz‐Monteagudo, M., Cordeiro, M. N. D. S., & Borges, F. (2007) · Journal of Computational Chemistry · doi:10.1002/jcc.20812 · Cited by 55 · see citing works
Wallach, I., Jaitly, N., & Lilien, R. (2010) · PLoS ONE · doi:10.1371/journal.pone.0012063 · Cited by 52 · see citing works
Xirasagar, S., Gustafson, S., Huang, C., et al. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btk045 · Cited by 48 · see citing works
Strategies for dealing with reactive intermediates in drug discovery and development.
Nassar, A.-E. F., & Lopez‐Anaya, A. (2004) · PubMed · Cited by 48 · see citing works
Data-Driven Methods to Discover Molecular Determinants of Serious Adverse Drug Events
Chiang, A. P.-F., & Butte, A. J. (2009) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2008.274 · Cited by 47 · see citing works
Local Kinetics and Dynamics of Xenobiotics
Pelkonen, O., Kapitulnik, J., Gundert-Remy, U., Boobis, A., & Stockis, A. (2008) · Critical Reviews in Toxicology · doi:10.1080/10408440802194931 · Cited by 39 · see citing works
Early toxicity screening strategies.
Greene, N., & Naven, R. T. (2009) · PubMed · Cited by 36 · see citing works
Bonnet, É., Bernard, J., Fauvel, J., et al. (2008) · AIDS Research and Human Retroviruses · doi:10.1089/aid.2007.0076 · Cited by 26 · see citing works
Assessing the Potential Toxicity of New Pharmaceuticals
Johnson, D., & Wolfgang, G. H. I. (2001) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026013395128 · Cited by 20 · see citing works
Self-citationQuantitative structure-toxicity relationships by accelerated cytotoxicity mechanism screening.
Siraki, A. G., Chevaldina, T., Moridani, M. Y., & O'Brien, P. J. (2004) · PubMed · Cited by 18 · see citing works
Future of Computational Toxicology: Broad Application into Human Disease and Therapeutics
Johnson, D. E., Rodgers, A. D., & Sudarsanam, S. (2006) · Computational Toxicology · doi:10.1002/9780470145890.ch26 · Cited by 6 · see citing works
Self-citationTrends in the Risks and Benefits to Patients With Cancer in Phase 1 Clinical Trials
Ross, S. D. (2005) · JAMA · doi:10.1001/jama.293.7.795-a · Cited by 5 · see citing works
Immunomodulatory therapies and the risk of opportunistic infections.
De, J., Da, S., & Park, B. K. (2010) · PubMed · Cited by 4 · see citing works
Predicting Drug Safety: Next Generation Solutions
Johnson, D. E. (2012) · Journal of Drug Metabolism & Toxicology · doi:10.4172/2157-7609.1000e106 · Cited by 4 · see citing works
Self-citationPharmacogenomics and adverse drug reactions; prospective screening for risk identification.
Johnson, D. E., Da, S., & Park, B. K. (2009) · PubMed · Cited by 4 · see citing works
Self-citationBiotherapeutic first-in-human dose selection: making use of preclinical markers
Johnson, D. E. (2010) · Expert Review of Clinical Pharmacology · doi:10.1586/ecp.10.5 · Cited by 3 · see citing works
Self-citationBiomarkers: the pièce de résistance of innovative medicines.
Johnson, D. E., Da, S., & Park, B. K. (2007) · PubMed · Cited by 1 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Wu, Y., & Wang, G. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19082358 · Cited by 193 · see citing works
- Authors
- Johnson, D. E.
- Affiliations
- University of Michigan (US)
- Corresponding author
- Johnson, Dale E
- ORCID
- Johnson, Dale E
- Venue
- Expert Review of Clinical Pharmacology
- Publisher
- Informa UK Limited
- Published
- 2013-03
- Volume
- 6
- Issue
- 2
- Pages
- 185-195
- ISSN
- 1751-2433 (ISSN-L)
- 1751-2441
- OpenAlex
- W2159109457
- Cited by
- 9
- References
- 51
- Citations by year
- 2023: 1 · 2019: 1 · 2018: 2 · 2017: 1 · 2015: 2 · 2014: 2
- FWCI
- 0.22
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Expert Review of Clinical Pharmacology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Toxicology › Pharmacovigilance and Adverse Drug Reactions
- Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- adverse drug reactions, drug safety prediction, translational research, small molecule drug candidates, patient context, pharmacovigilance, cloud computing, drug development
- MeSH terms
- Animals, Clinical Trials as Topic, Computer Simulation, Humans, Adverse Drug Reaction Reporting Systems, Drug Discovery, Pharmacovigilance, Drug-Related Side Effects and Adverse Reactions
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
