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Drug approval prediction based on the discrepancy in gene perturbation effects between cells and humans
Minhyuk Park1, Donghyo Kim1, Inhae Kim2
1Department of Life Sciences, Pohang University of Science and Technology, Pohang, South Korea.
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 To validate the risk of drug targets with the cells/humans discrepancy, we examined the targets of failed and withdrawn drugs due to safety problems.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 Furthermore, genes with the cells/humans discrepancy were related to drugs withdrawn due to severe side effects.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).
Insights
Predicting drug approval is improved by analyzing the difference in gene effects between cells and humans. This approach helps identify potential safety failures in clinical trials, reducing drug development costs and improving patient outcomes.
Area of Science:
- Pharmacology and Toxicology
- Computational Biology
- Drug Discovery
Background:
- Clinical trial drug safety failures stem from discrepancies between in vitro (cell-based) and human (clinical) drug target effects.
- These discrepancies increase drug development costs and negatively impact patient quality of life.
- A predictive model is needed to account for cell-human differences and reduce clinical trial attrition.
Purpose of the Study:
- To develop a machine learning framework for predicting drug approval in clinical trials.
- To quantify the cells/humans discrepancy in drug target perturbation effects.
- To reduce drug attrition rates by identifying potential safety failures early.
Main Methods:
- Utilized a machine learning framework to predict drug approval based on gene perturbation effects.
- Analyzed CRISPR-Cas9 knockout and loss-of-function mutation data to evaluate cell-human discrepancies in gene perturbation.
- Examined drug targets of failed/withdrawn drugs to validate the risk associated with cell-human discrepancies.
- Integrated chemical properties with gene perturbation data to enhance prediction accuracy.
Main Results:
- A correlation was found between drug approval rates and the cells/humans discrepancy in gene perturbation effects.
- Genes showing tolerance to perturbation in cells but intolerance in humans were linked to failed drug targets.
- The cells/humans discrepancy in gene perturbation was associated with drugs withdrawn due to severe side effects.
- Integrating chemical information improved the prediction of drug approval.
Conclusions:
- The cells/humans discrepancy in gene perturbation effects is a key factor in predicting drug approval.
- This discrepancy helps explain safety failures observed in clinical trials.
- The developed model offers a novel approach to enhance drug safety assessment and reduce attrition.
