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Updated: Sep 1, 2026

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms
Published on: December 9, 2015
Anticipating designer drug-resistant cancer cells
Mark L Frangione1, John H Lockhart1, Daniel T Morton1
1Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL, USA.
Abstract:
Successful use of anticancer designer drugs is likely to depend on simultaneous combinations of these drugs to minimize the development of resistant cancer cells. Considering the knowledge base of cancer signaling pathways, mechanisms of designer drug resistance should be anticipated, and early clinical trials could be designed to include arms that combine new drugs specifically with currently US Food and Drug Administration (FDA)-approved drugs expected to blunt alternative signaling pathways. In this review, we indicate examples of alternative signal pathways for recent anticancer drugs, and the use of original, Python-based software to systematically identify signaling pathways that could facilitate resistance to drugs targeting a particular protein. Pathway alternatives can be assessed at http://www.alternativesignalingpathways.com, developed with this review article.
Insights
Combining anticancer designer drugs with FDA-approved drugs can minimize cancer cell resistance. This strategy anticipates alternative signaling pathways, crucial for effective cancer treatment development.
Area of Science:
- Oncology
- Pharmacology
- Bioinformatics
Background:
- Development of anticancer designer drugs requires strategies to overcome drug resistance.
- Cancer cells can develop resistance through alternative signaling pathways.
- Simultaneous drug combinations are essential for effective cancer therapy.
Purpose of the Study:
- To review alternative signaling pathways for recent anticancer drugs.
- To introduce a Python-based software for identifying resistance pathways.
- To guide the design of early clinical trials for combination anticancer therapies.
Main Methods:
- Systematic identification of signaling pathways using original Python-based software.
- Analysis of known cancer signaling pathways and drug resistance mechanisms.
- Review of FDA-approved drugs for potential combination therapies.
Main Results:
- Examples of alternative signaling pathways facilitating resistance to anticancer drugs are presented.
- The developed software systematically identifies potential resistance pathways.
- A web-based tool (http://www.alternativesignalingpathways.com) is available for pathway assessment.
Conclusions:
- Anticipating and targeting alternative signaling pathways is key to overcoming drug resistance.
- Combination therapy using new designer drugs with FDA-approved agents can enhance efficacy.
- The developed software and web tool aid in designing effective clinical trials for cancer treatment.
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