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Updated: Dec 28, 2025
Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
Published on: December 11, 2016
Entering the era of computationally driven drug development
Neha Maharao1, Victor Antontsev1, Matthew Wright2
1VeriSIM Life Inc., San Francisco, CA, USA.
Abstract:
Historically, failure rates in drug development are high; increased sophistication and investment throughout the process has shifted the reasons for attrition, but the overall success rates have remained stubbornly and consistently low. Only 8% of new entities entering clinical testing gain regulatory approval, indicating that significant obstacles still exist for efficient therapeutic development. The continued high failure rate can be partially attributed to the inability to link drug exposure with the magnitude of observed safety and efficacy-related pharmacodynamic (PD) responses; frequently, this is a result of nonclinical models exhibiting poor prediction of human outcomes across a wide range of disease conditions, resulting in faulty evaluation of drug toxicology and efficacy. However, the increasing quality and standardization of experimental methods in preclinical stages of testing has created valuable data sets within companies that can be leveraged to further improve the efficiency and accuracy of preclinical prediction for both pharmacokinetics (PK) and PD. Models of Quantitative structure-activity relationships (QSAR), physiologically based pharmacokinetics (PBPK), and PK/PD relationships have also improved efficiency. Founded on a core understanding of biochemistry and physiological interactions of xenobiotics, these in silico methods have the potential to increase the probability of compound success in clinical trials. Integration of traditional computational methods with machine-learning approaches and existing internal pharma databases stands to make a fundamental impact on the speed and accuracy of predictions during the process of drug development and approval.
Insights
Drug development has a high failure rate due to poor prediction of human outcomes. Integrating computational methods like QSAR and PBPK with machine learning can improve preclinical predictions and increase clinical trial success rates.
Area of Science:
- Pharmacology
- Drug Development
- Computational Chemistry
Background:
- Drug development exhibits persistently high attrition rates, with only 8% of new entities reaching regulatory approval.
- A key challenge is linking drug exposure to pharmacodynamic (PD) responses, often due to inadequate prediction of human outcomes by nonclinical models.
- This leads to inaccurate assessments of drug toxicology and efficacy, contributing to development failures.
Purpose of the Study:
- To address the high failure rates in drug development by improving the accuracy of preclinical predictions.
- To leverage advancements in computational methods and data standardization to enhance the prediction of pharmacokinetics (PK) and PD responses.
- To increase the probability of compound success in clinical trials through more reliable preclinical evaluations.
Main Methods:
- Utilizing improved quality and standardization of experimental methods in preclinical testing to generate valuable datasets.
- Applying computational models such as Quantitative Structure-Activity Relationships (QSAR) and physiologically based pharmacokinetics (PBPK).
- Integrating traditional computational techniques with machine learning approaches and internal pharmaceutical databases.
Main Results:
- Enhanced computational methods show potential for improving the efficiency and accuracy of preclinical predictions for PK and PD.
- These in silico approaches, grounded in biochemistry and xenobiotic interactions, can enhance the prediction of compound behavior in humans.
- The integration of diverse data and computational strategies promises to fundamentally impact prediction speed and accuracy.
Conclusions:
- Advancements in computational methods offer a promising avenue to mitigate high drug development failure rates.
- Improved in silico modeling and data integration can lead to more accurate preclinical assessments of drug safety and efficacy.
- This strategic integration has the potential to significantly accelerate and de-risk the drug development and approval process.
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