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Updated: Jun 8, 2025
Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms
Published on: December 9, 2015
Beyond IC50-A computational dynamic model of drug resistance in enzyme inhibition treatment
J Roadnight Sheehan1, Astrid S de Wijn1, Thales Souza Freire2
1Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, Trondheim, Norway.
Abstract:
Resistance to therapy is a major clinical obstacle to treatment of cancer and communicable diseases. Drug selection in treatment of patients where the disease is showing resistance to therapy is often guided by IC50 or fold-IC50 values. In this work, through a model of the treatment of chronic myeloid leukaemia (CML), we contest using fold-IC50 values as a guide for treatment selection. CML is a blood cancer that is treated with Abl1 inhibitors, and is often seen as a model for targeted therapy and drug resistance. Resistance to the first-line treatment occurs in approximately one in four patients. The most common cause of resistance is mutations in the Abl1 enzyme. Different mutant Abl1 enzymes show resistance to different Abl1 inhibitors and the mechanisms that lead to resistance for various mutation and inhibitor combinations are not fully known, making the selection of Abl1 inhibitors for treatment a difficult task. We developed a model based on information of catalysis, inhibition and pharmacokinetics, and applied it to study the effect of three Abl1 inhibitors on mutants of the Abl1 enzyme. From this model, we show that the relative decrease of product formation rate (defined in this work as "inhibitory reduction prowess") is a better indicator of resistance than an examination of the size of the product formation rate or fold-IC50 values for the mutant. We also examine current ideas and practices that guide treatment choice and suggest a new parameter for selecting treatments that could increase the efficacy and thus have a positive impact on patient outcomes.
Insights
This study challenges using fold-IC50 values for selecting cancer therapies. A new metric, "inhibitory reduction prowess," better predicts drug resistance in chronic myeloid leukemia (CML) treatment.
Area of Science:
- Oncology
- Pharmacology
- Biochemistry
Background:
- Therapy resistance is a significant challenge in treating cancer and infectious diseases.
- Drug selection is often guided by IC50 or fold-IC50 values, particularly in cases of resistance.
- Chronic myeloid leukemia (CML) serves as a model for targeted therapy and drug resistance, with approximately 25% of patients developing resistance to first-line treatments.
Purpose of the Study:
- To evaluate the efficacy of using fold-IC50 values for guiding drug selection in resistant CML.
- To develop and apply a predictive model for assessing Abl1 inhibitor effectiveness against resistant Abl1 enzyme mutants.
- To propose a novel parameter for treatment selection that improves therapeutic efficacy and patient outcomes.
Main Methods:
- Development of a computational model integrating catalysis, inhibition, and pharmacokinetic data.
- Application of the model to simulate the effects of three Abl1 inhibitors on various Abl1 enzyme mutants.
- Comparison of the predictive power of "inhibitory reduction prowess" against traditional metrics like fold-IC50.
Main Results:
- The study demonstrates that "inhibitory reduction prowess" is a more effective indicator of drug resistance than fold-IC50 values.
- Analysis revealed that existing methods for guiding treatment selection may not be optimal for resistant mutations.
- The model provides insights into the complex mechanisms of resistance for different mutation-inhibitor combinations.
Conclusions:
- Fold-IC50 values are insufficient for guiding drug selection in resistant CML.
- "Inhibitory reduction prowess" offers a superior metric for predicting treatment response and selecting effective Abl1 inhibitors.
- The proposed new parameter has the potential to enhance treatment efficacy and improve patient outcomes in targeted cancer therapy.
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