More than 50 years after President Nixon declared the “War on Cancer” in 1971, the disease that was only whispered back then still causes the second-most deaths in the U.S., after heart disease.

Although there is no “cure” yet, new anti-cancer drugs help prolong the lives of many patients. Still, some people fear the agonizing therapy, which can cause significant side effects: anemia, edema, fatigue, hair loss, bleeding, infection, peripheral neuropathy, pain, and organ-related inflammation.

The most common cancer drug delivery system is intravenous bolus injections (IVs), which are not targeted, meaning they are not administered at the site of the tumors and only a smaller amount of the drug reaches them. Newer targeted methods, such as robotics, nanoparticles, and implants help minimize side effects because they ensure the chemotherapy drugs or other anti-cancer drugs are delivered directly to the tumors instead of affecting healthy cells in the rest of the body. With the controlled release of the drugs, the patient only receives the medication when needed and in smaller doses, rather than a large IV dose.

How AI and Agentic AI Enhance Cancer Drug Delivery 

Although many people probably think artificial intelligence (AI) emerged as a key player when ChatGPT debuted a few years ago, AI has been around for decades. According to the Journal of Biomedical Research,¹ the medical field used AI in the 1960s to develop drugs.

However, today, developing new anti-cancer drugs means nothing if they can’t be delivered effectively to maximize the optimum results. The article adds: “The use of AI in drug delivery has the potential to accelerate the development of new drug delivery systems and improve drug efficacy. In brief, drug delivery means delivering the right amount of medication to the right place at the right time.”

AI is revolutionizing cancer drug delivery by combining advanced imaging, genetic analysis, and machine learning algorithms. AI-guided imaging identifies the tumor’s location, ensuring targeted drug delivery and avoiding healthy tissue. Machine learning models and deep learning neural networks analyze the patient’s genetic makeup and tumor characteristics to customize drugs and dosages. AI models can determine how patients will respond to the therapy, eliminating a trial-and-error approach.

Nanotechnology Delivery Systems

ScienceDirect.com, which explains that nanoparticles bind to the tumor, said this drug delivery technology “has gained significant attention in recent years due to [its] unique properties and potential applications in cancer treatment.^”2

The medical field has gravitated toward nanoparticle-based drug delivery systems because they have “shown promise in improving cancer treatment by selectively delivering drugs to cancer cells, minimizing damage to healthy tissues.”

Oncologists are currently using several nanotechnology drug delivery systems to treat cancer patients, including:

• Magnetic nanoparticles, which use AI models and machine learning algorithms to optimize the nanoparticle design and enable drug delivery guided by magnetic fields 
• AI-powered smart nanoparticles, “which can respond to biological cues or be guided by them.”³ They are “emerging as a promising drug delivery platform for precise cancer treatment.”
• Magnetic drug delivery systems “use external or internal magnets to increase the accumulation of therapeutic elements contained in nanoparticles to fight pathologies in specific parts of the body.”⁴ AI modeling has advanced the optimization of the targeting and timed release of the drug by using magnetic nanoparticles, which use more of the drug on the cancerous cells. 

Even before the anti-cancer drug is delivered, AI will analyze the patient’s medical history, including genetic profiles, tumor size, and tumor shape, to recommend the best possible healthcare plan and delivery systems. 

Researchers are exploring how Agentic AI, the next progression of AI, can revolutionize the delivery of cancer drugs. The key difference between the two is that the latter adjusts doses and drug combinations for cancer patients with minimal human involvement. 

Unlike AI, which requires more human oversight, agentic AI will autonomously regulate cancer treatment in real time. By monitoring biomarkers and tumor progression, this advanced technology can process countless drug interactions to select the most effective drug combination. Its continuous learning ability optimizes nanoparticle properties, minimizes side effects on healthy cells, and should generate better patient outcomes with limited manual adjustments.

Robotics for Cancer Drug Delivery

Robotic microbots are being used to deliver cancer drugs directly to tumors. AI helps with patient positioning optimization, which puts the body in the best position to allow the microbots to reach the optimum location in the body to release the anti-cancer drug.

Recent developments include: 

• Researchers at the University of Galway and Massachusetts Institute of Technology (MIT) have developed an AI-powered “soft robotic implant” to adjust drug dosages based on the biological environment. The device “can administer a drug—while also sensing when the body is beginning to reject it—and use AI to change its shape” to continue treatment.
• University of California San Diego engineers “have developed microscopic robots, known as microrobots, capable of swimming through the lungs to deliver cancer-fighting medication directly to metastatic tumors.”

Benefits for Patients, Doctors, and Hospitals

For patients, AI-driven drug delivery systems should reduce side effects because the more precise method minimizes systemic toxicity. AI should also optimize drug doses, leading to better outcomes because the cancer treatment plan is based on patient-specific data, such as genetic and metabolic markers. 

For doctors and oncologists, AI-powered systems provide enhanced decision-making data by analyzing patient history and optimizing drug regimens. By incorporating AI automation, the doctor can focus on patient care rather than the administration of drugs.

For hospitals and pharmaceutical organizations, AI-powered drug delivery reduces drug waste, ensures regulatory compliance for drug administration protocols, and results in better patient outcomes. 

Implementation Challenges and Barriers

As with the implementation of any new technology, healthcare organizations must deal with the challenges, including:

• Integration with existing healthcare systems
• Regulatory and compliance requirements
• High costs and investment barriers
• Data security for sensitive medical records
• AI bias and ethical concerns
• Technical and scientific challenges
• Resistance by medical professionals

Hospitals and doctors bear the critical responsibility of ensuring AI model accuracy and reliability, validating real-time dose adjustments, and guaranteeing the precise targeting, stability, and timed drug release promised by AI, agentic AI, nanotechnology and robotics, given the life-and-death consequences involved.

With these technologies rapidly evolving, it appears we are winning another battle in the continuing “War on Cancer.” We may never defeat the disease but higher survival rates are nevertheless a victory. In the future, we should see wider adoption of these technologies in clinical practices. To stay competitive, hospitals must start evaluating and testing these methods to determine the impact AI and agentic AI will have on targeted drug delivery methods.

References

1. bit.ly/mposoftware04251
2. bit.ly/mposoftware04252
3. go.nature.com/4iZjBvY
4. bit.ly/mposoftware04253

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Deepak Borole, a project manager at Chetu, a software solutions and support services provider, oversees the company’s healthcare portfolios.