Drug approval prediction based on the discrepancy in gene perturbation effects between cells and humansResearch in context
Summary: Background: Poor translation between in vitro and clinical studies due to the cells/humans discrepancy in drug target perturbation effects leads to safety failures in clinical trials, thus increasing drug development costs and reducing patients’ life quality. Therefore, developing a predic...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-08-01
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| Series: | EBioMedicine |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352396423002700 |
| _version_ | 1797746012982345728 |
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| author | Minhyuk Park Donghyo Kim Inhae Kim Sin-Hyeog Im Sanguk Kim |
| author_facet | Minhyuk Park Donghyo Kim Inhae Kim Sin-Hyeog Im Sanguk Kim |
| author_sort | Minhyuk Park |
| collection | DOAJ |
| description | Summary: Background: Poor translation between in vitro and clinical studies due to the cells/humans discrepancy in drug target perturbation effects leads to safety failures in clinical trials, thus increasing drug development costs and reducing patients’ life quality. Therefore, developing a predictive model for drug approval considering the cells/humans discrepancy is needed to reduce drug attrition rates in clinical trials. Methods: Our machine learning framework predicts drug approval in clinical trials based on the cells/humans discrepancy in drug target perturbation effects. To evaluate the discrepancy to predict drug approval (1404 approved and 1070 unapproved drugs), we analysed CRISPR-Cas9 knockout and loss-of-function mutation rate-based gene perturbation effects on cells and humans, respectively. To validate the risk of drug targets with the cells/humans discrepancy, we examined the targets of failed and withdrawn drugs due to safety problems. Findings: Drug approvals in clinical trials were correlated with the cells/humans discrepancy in gene perturbation effects. Genes tolerant to perturbation effects on cells but intolerant to those on humans were associated with failed drug targets. Furthermore, genes with the cells/humans discrepancy were related to drugs withdrawn due to severe side effects. Motivated by previous studies assessing drug safety through chemical properties, we improved drug approval prediction by integrating chemical information with the cells/humans discrepancy. Interpretation: The cells/humans discrepancy in gene perturbation effects facilitates drug approval prediction and explains drug safety failures in clinical trials. Funding: S.K. received grants from the Korean National Research Foundation (2021R1A2B5B01001903 and 2020R1A6A1A03047902) and IITP (2019-0-01906, Artificial Intelligence Graduate School Program, POSTECH). |
| first_indexed | 2024-03-12T15:31:01Z |
| format | Article |
| id | doaj.art-f55c24d03c434bb698c113fbb935d57a |
| institution | Directory Open Access Journal |
| issn | 2352-3964 |
| language | English |
| last_indexed | 2024-03-12T15:31:01Z |
| publishDate | 2023-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | EBioMedicine |
| spelling | doaj.art-f55c24d03c434bb698c113fbb935d57a2023-08-10T04:34:31ZengElsevierEBioMedicine2352-39642023-08-0194104705Drug approval prediction based on the discrepancy in gene perturbation effects between cells and humansResearch in contextMinhyuk Park0Donghyo Kim1Inhae Kim2Sin-Hyeog Im3Sanguk Kim4Department of Life Sciences, Pohang University of Science and Technology, Pohang, South KoreaDepartment of Life Sciences, Pohang University of Science and Technology, Pohang, South KoreaImmunoBiome Inc., Pohang, South KoreaDepartment of Life Sciences, Pohang University of Science and Technology, Pohang, South Korea; ImmunoBiome Inc., Pohang, South KoreaDepartment of Life Sciences, Pohang University of Science and Technology, Pohang, South Korea; Corresponding author. Department of Life Sciences, Pohang University of Science and Technology, Pohang, 790-784, South Korea.Summary: Background: Poor translation between in vitro and clinical studies due to the cells/humans discrepancy in drug target perturbation effects leads to safety failures in clinical trials, thus increasing drug development costs and reducing patients’ life quality. Therefore, developing a predictive model for drug approval considering the cells/humans discrepancy is needed to reduce drug attrition rates in clinical trials. Methods: Our machine learning framework predicts drug approval in clinical trials based on the cells/humans discrepancy in drug target perturbation effects. To evaluate the discrepancy to predict drug approval (1404 approved and 1070 unapproved drugs), we analysed CRISPR-Cas9 knockout and loss-of-function mutation rate-based gene perturbation effects on cells and humans, respectively. To validate the risk of drug targets with the cells/humans discrepancy, we examined the targets of failed and withdrawn drugs due to safety problems. Findings: Drug approvals in clinical trials were correlated with the cells/humans discrepancy in gene perturbation effects. Genes tolerant to perturbation effects on cells but intolerant to those on humans were associated with failed drug targets. Furthermore, genes with the cells/humans discrepancy were related to drugs withdrawn due to severe side effects. Motivated by previous studies assessing drug safety through chemical properties, we improved drug approval prediction by integrating chemical information with the cells/humans discrepancy. Interpretation: The cells/humans discrepancy in gene perturbation effects facilitates drug approval prediction and explains drug safety failures in clinical trials. Funding: S.K. received grants from the Korean National Research Foundation (2021R1A2B5B01001903 and 2020R1A6A1A03047902) and IITP (2019-0-01906, Artificial Intelligence Graduate School Program, POSTECH).http://www.sciencedirect.com/science/article/pii/S2352396423002700Machine learningDrug approvalDrug safetyClinical translationGene perturbation effectDiscrepancy |
| spellingShingle | Minhyuk Park Donghyo Kim Inhae Kim Sin-Hyeog Im Sanguk Kim Drug approval prediction based on the discrepancy in gene perturbation effects between cells and humansResearch in context EBioMedicine Machine learning Drug approval Drug safety Clinical translation Gene perturbation effect Discrepancy |
| title | Drug approval prediction based on the discrepancy in gene perturbation effects between cells and humansResearch in context |
| title_full | Drug approval prediction based on the discrepancy in gene perturbation effects between cells and humansResearch in context |
| title_fullStr | Drug approval prediction based on the discrepancy in gene perturbation effects between cells and humansResearch in context |
| title_full_unstemmed | Drug approval prediction based on the discrepancy in gene perturbation effects between cells and humansResearch in context |
| title_short | Drug approval prediction based on the discrepancy in gene perturbation effects between cells and humansResearch in context |
| title_sort | drug approval prediction based on the discrepancy in gene perturbation effects between cells and humansresearch in context |
| topic | Machine learning Drug approval Drug safety Clinical translation Gene perturbation effect Discrepancy |
| url | http://www.sciencedirect.com/science/article/pii/S2352396423002700 |
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