From Side Effect to Cure
How Computer Magic is Repurposing Existing Drugs to Fight Cancer
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The Drug Discovery Dilemma: Why Cancer Treatment Needs a New Approach
Cancer remains one of the most formidable challenges in modern medicine, with more than 200 different types identified and an estimated 9.6 million cancer-related deaths reported globally each year 1 . The traditional approach to cancer drug development—designing new molecules from scratch—is notoriously time-consuming and expensive, requiring approximately 12-15 years and $2.5 billion on average to bring a single drug to market 2 .
This process, known as de novo drug discovery, has a staggering failure rate of approximately 95% for anticancer agents, meaning only 5 out of 100 potential drugs that enter development eventually gain approval 3 .
Despite tremendous advances in cell biology, oncology, and computer sciences, cancer continues to be a leading cause of death worldwide 4 . The pharmaceutical industry faces what experts call a "productivity crisis" in research and development, with efficiency declining despite impressive scientific and technological advances—a phenomenon known as Eroom's Law (Moore's Law spelled backward) 5 .
Failure rate of traditional anticancer drug development
Time required for traditional drug development
In this challenging landscape, scientists have turned to an innovative strategy called drug repositioning (also known as drug repurposing), which finds new therapeutic uses for existing medicines. This approach leverages drugs that have already been approved for other conditions, taking advantage of their established safety profiles and known pharmacokinetics to accelerate their application in cancer treatment 3 . Structure-based drug repositioning represents a particularly promising frontier, using computational tools to understand how existing drugs might interact with cancer-related targets at the molecular level.
What is Drug Repositioning and How Does It Work?
The Basics of Giving Old Drugs New Jobs
Drug repositioning is the process of identifying new therapeutic applications for existing drugs that are beyond the scope of their original medical use 3 . This approach differs from traditional drug discovery in several key ways:
Reduced Timeline
Repositioned drugs can reach patients in 3-9 years compared to 12-15 years for new drugs
Lower Cost
Development costs are approximately one-third of traditional drug discovery
Decreased Risk
Safety profiles are already established, reducing unexpected adverse effects
Higher Success Rate
Repositioned candidates have higher likelihood of reaching approval
The concept isn't entirely new—many famous examples exist in medicine. Sildenafil (Viagra) was originally developed for angina but found unexpected application in erectile dysfunction 1 . Thalidomide, initially marketed for morning sickness but withdrawn due to teratogenic effects, was later repurposed for leprosy and multiple myeloma 5 . Minoxidil, first an antihypertensive medication, now treats hair loss as Rogaine 3 .
The Structure-Based Approach: Molecular Matchmaking
Structure-based drug repositioning takes this concept a step further by examining how the three-dimensional structure of a drug molecule might interact with cancer-related proteins. Using computational methods, scientists can predict whether a drug developed for one condition might effectively target proteins involved in cancer pathways 4 .
This process relies on two key algorithmic approaches: docking (predicting how a drug molecule fits into a protein's binding site) and binding site comparisons (identifying similarities between different proteins' drug-binding pockets) 5 . The underlying principle is that many drugs exhibit polypharmacology—the ability to bind multiple targets—often because different proteins share similar structural features at their binding sites 5 .
A Closer Look: The Proteasome Inhibitor Discovery Experiment
Hunting for Hidden Cancer Fighters
One compelling example of structure-based drug repositioning comes from a 2024 study that set out to identify new proteasome inhibitors—drugs that disrupt the cellular machinery responsible for breaking down proteins in cancer cells 2 . The proteasome is a validated cancer target, with drugs like bortezomib already approved for multiple myeloma and mantle cell lymphoma. However, existing proteasome inhibitors have limitations, including difficulty crossing the blood-brain barrier and the development of treatment resistance 2 .
Researchers used an integrated approach combining transcriptomics data (information about gene expression) with structure-based virtual screening (computer modeling of drug-target interactions). They started by identifying a consistent 12-gene signature that cells expressed when treated with known proteasome inhibitors like bortezomib, MG-132, and MLN-2238 2 .
Methodology: Connecting the Dots Between Drugs and Cancer
Gene signature identification
They analyzed data from the Connectivity Map (CMap) and iLINCS databases to find genes consistently affected by proteasome inhibitors across different cell lines and treatment conditions 2 .
Similarity searching
They used computational tools to find other FDA-approved drugs that produced similar gene expression patterns to the known proteasome inhibitors 2 .
Virtual screening
They computationally docked these candidate drugs into the three-dimensional structure of the proteasome's β5 subunit (a key catalytic site) to predict which might bind effectively 2 .
Experimental validation
The top candidates were tested in laboratory experiments to confirm their ability to inhibit proteasome activity, cause accumulation of ubiquitinated proteins (a sign of proteasome inhibition), and kill cancer cells 2 .
Remarkable Findings: Six New Proteasome Inhibitors
| Compound | Original Use | Proteasome Inhibition | Cytotoxic to Cancer Cells |
|---|---|---|---|
| (-)-Kinetin-riboside | Plant growth hormone | β1, β2, and β5 sites | Yes |
| Manumycin-A | Antibiotic | β1, β2, and β5 sites | Yes |
| Puromycin dihydrochloride | Antibiotic/research reagent | β1, β2, and β5 sites | Yes |
| Resistomycin | Antibiotic | β1, β2, and β5 sites | Yes |
| Tegaserod maleate | Irritable bowel syndrome | β1, β2, and β5 sites | Yes |
| Thapsigargin | Research tool (calcium signaling) | β1, β2, and β5 sites | Yes |
The study identified six compounds with proteasome inhibitor properties, including tegaserod maleate (formerly used for irritable bowel syndrome), manumycin-A (an antibiotic), and puromycin dihydrochloride (a research reagent) 2 . Laboratory experiments confirmed that these compounds inhibited multiple catalytic sites of the proteasome (β1, β2, and β5), caused accumulation of ubiquitinated proteins, and increased expression of HMOX1—a gene known to be responsive to proteasome stress 2 .
These findings were particularly significant because they demonstrated that drugs developed for completely different purposes could have previously unrecognized activity against an important cancer target. The integrated approach of combining transcriptomic data with structure-based screening proved effective for identifying these repurposing opportunities 2 .
The Scientific Toolkit: Technologies Powering Drug Repositioning
| Method | Function | Application in Repositioning |
|---|---|---|
| Molecular Docking | Predicts how drugs bind to target proteins | Virtual screening of drug libraries against cancer targets |
| Binding Site Comparison | Identifies similar binding pockets across proteins | Finds unexpected targets for existing drugs |
| Molecular Dynamics Simulations | Models atomic movements over time | Assesses stability of drug-target interactions |
| Pharmacophore Modeling | Identifies essential structural features for activity | Optimizes drugs for new cancer targets |
| AI/Machine Learning | Finds patterns in large chemical and biological datasets | Predicts new drug-target interactions |
The structure-based repositioning process begins with molecular docking, where researchers use computer programs to virtually "test" how existing drugs might fit into the three-dimensional structures of proteins known to be important in cancer 1 . Advanced techniques like cryo-electron microscopy (cryo-EM) have revolutionized this field by providing high-resolution views of potential drug targets, especially G protein-coupled receptors (GPCRs) like chemokine receptors that play important roles in cancer progression 6 .
Repository of Chemical Agents
>200,000 synthetic and natural compounds for screening libraries for identifying new anticancer activities 7
Natural Products Repository
~200,000 extracts from plants, marine organisms, and microbes as a source of diverse chemical structures with biological activity 7
Another powerful approach involves analysis of gene expression patterns. Researchers compare how cancer cells respond to known drugs, then look for other compounds that produce similar genetic "signatures" 2 . This method led to the identification of several compounds with proteasome inhibitor properties similar to established cancer drugs like bortezomib 2 .
Challenges and Future Directions
Limitations of the Structure-Based Approach
Incomplete Structural Data
Although the number of protein structures in the Protein Data Bank (PDB) has grown exponentially, many important drug targets remain without experimentally determined structures 5 .
Dynamic Nature of Proteins
Proteins are flexible molecules that change shape, but static structures may not capture these important movements 1 .
Limited Target-Indication Links
Understanding which targets are relevant to which diseases remains a significant knowledge gap 5 .
Off-Target Effects
While polypharmacology can be beneficial, unintended interactions may cause adverse effects 1 .
The Future: AI and Advanced Technologies
The future of structure-based drug repositioning looks bright, thanks to advances in artificial intelligence (AI) and machine learning. These technologies can analyze vast amounts of chemical and biological data to identify patterns that might escape human researchers 1 . For example, AI algorithms can predict new drug-target interactions based on structural similarities or gene expression patterns 1 .
Network pharmacology—which views diseases as disruptions in complex cellular networks rather than isolated malfunctions of single targets—represents another promising approach 1 . This perspective recognizes that many drugs act on multiple targets simultaneously, which may be particularly advantageous for treating complex diseases like cancer 1 .
The integration of multi-omics data (genomics, transcriptomics, proteomics, etc.) with structural information will likely yield even more powerful repositioning strategies in the coming years 3 . As these technologies advance, they will help researchers identify optimal drug combinations and personalized treatment approaches based on individual patient characteristics.
Conclusion: The Future of Cancer Drug Discovery
Structure-based drug repositioning represents a paradigm shift in how we approach cancer treatment. Instead of starting from scratch with new molecules, researchers can now use computational tools to mine the existing pharmacopeia for hidden anticancer potential. This approach leverages the considerable investment already made in drug development while offering faster, cheaper, and potentially safer routes to new cancer therapies.
"The successful identification of proteasome inhibitors from non-cancer drugs illustrates the power of this approach 2 . As structural biology techniques continue to advance—especially cryo-EM and computational prediction methods—and as AI algorithms become more sophisticated, we can expect many more repositioning successes in the future."
While challenges remain, particularly in understanding the complex relationship between drug targets and disease indications, the field is progressing rapidly. Structure-based drug repositioning offers hope for addressing the productivity crisis in pharmaceutical research while bringing new treatment options to cancer patients faster than ever before.
As research continues, this innovative approach may eventually make cancer a more manageable disease, transforming it from a deadly threat to a chronic condition that can be controlled with cleverly repurposed medicines.