Advances in AI are significantly enhancing the accuracy of diagnostics for cancer patients and transforming oncology drug development.
AI and machine learning are being used to identify early-stage cancers, precisely locate specific types, suggest optimum therapeutic options, characterise tumours, and predict immunotherapy responses in individual patients.
Furthermore, these tools can assist researchers in accurately determining if a particular drug would successfully engage with a targeted cancer-related protein – speeding up drug discovery processes significantly.
Accelerating the development of oncology drugs
The capacity of AI systems to understand and analyse large volumes of imaging and non-imaging data has the potential to accelerate the development of oncology treatments. The pharmaceutical industry is leveraging these emerging technologies to support traditional screening studies, identify biomarkers predictive of therapeutic response, and select optimal treatments.
For instance, oncology-focused biotech firm Lantern Pharma has developed a proprietary machine-learning-based platform to analyse patient data – such as genetic makeup and health issues – to accurately organise patients for specific cancer treatments. Similarly, Massive Bio has recently launched an AI-powered platform that enables oncologists to identify more cancer treatment options for patients, including recently approved drugs and active clinical trials.
Furthermore, the Institute of Cancer Research (ICR), in conjunction with IDIBELL and Vivan Therapeutics, is using Big Data and AI to develop novel targeted cancer treatments that can overcome drug resistance, with a special focus on KRAS, a well-known cancer-driving protein.
To determine possible targets for cancer therapy, AI systems can analyse extensive chemical and biological databases. This allows researchers and medical professionals to confirm the most promising candidates for additional testing, saving time and resources during drug development. In addition, AI algorithms can help sponsors design more efficient and targeted clinical trials, potentially leading to faster approvals for drugs.
According to the GlobalData report, Artificial Intelligence in Pharma, oncology is the leading therapy area for AI-developed drugs. These treatments are expected to dominate future drug launches and sales between 2025 and 2029. GlobalData estimates that the global oncology therapeutics market will reach a value of $343.7bn by 2033, with drugs for breast, colorectal, lung, prostate, and pancreatic cancers having the largest market share.
How AI is being used in cancer diagnostics
In the healthcare sector, advanced technology can substantially improve the accuracy of diagnostics. AI algorithms can analyse medical images such as CT scans and mammograms, as well as detect tumours or any abnormalities with higher accuracy than traditional methods.
Experts can utilise the capabilities of AI models to predict the risk of patients developing cancer in the future. Using information from genetic data and medical imaging, oncology teams can analyse tumour gene sequencing data to predict the primary source of a patient’s cancer. This can be particularly useful in cases where the source of the tumour cannot be determined, helping guide treatment decisions and improving outcomes.
As of January 2023, the FDA has approved approximately 520 AI and ML algorithms for medical use. And 122 approvals in radiology account for 87% of the devices authorised in 2022.
An AI tool recently developed by scientists at the Mass General Cancer Center and the Massachusetts Institute of Technology in Cambridge can project the patient’s risk of developing lung cancer. According to one study, the technology named Sybil can identify whether a person could develop lung cancer in the next year. Accuracy rates were between 86% and 94%.
In Scotland, trials at the Aberdeen Royal Infirmary are investigating how AI can assist radiologists in reviewing thousands of mammograms each year. The Gemini project at NHS Grampian used an AI software called Mia to perform further evaluations after mammography scan assessments. An early-stage patient identified in the study has since undergone surgery.
Meanwhile, researchers at the Royal Marsden NHS Foundation Trust, Imperial College London, and the Institute of Cancer Research, London are all developing AI technology to determine whether growths detected on CT scans are malignant. A radiomics-based AI algorithm was created using CT scans of 500 patients with large lung nodules. Medical photos can be analysed by technology to extract information that the human eye is not capable of seeing. Although the Libra study has shown promising results, further testing is needed before adoption by healthcare systems.
Patients are also increasingly supportive of the clinical uses of AI. A GlobalData poll of patients’ perspectives on AI in clinical practice found that more than half of the 574 respondents were comfortable with medical practitioners using AI to support patient referrals, diagnoses, and treatment.
Enabling access to oncology drugs
Despite the growing success of early cancer detection using AI and the development of new drugs, restrictions in access to oncology products can have a serious impact on a patient’s long-term health in fighting the disease.
Furthermore, there may be obstacles to cancer treatments such as geographical location, regulatory issues, healthcare systems, and insurance coverage. The cost of these treatments is also a significant barrier.
This is the case for Keytruda, a market-leading drug many predict will be the most-sold cancer treatment in 2023. Keytruda has proven effective at slowing down progression in multiple types of cancers at different stages, including breast, melanoma, kidney, and Hodgkin’s lymphoma, offering hope to patients at varying stages of the disease. However, with a list price as high as $150,000 a year, the cost of Keytruda is an issue for many patients.
Similarly, targeted cancer drug Bevacizumab, also known as Avastin, has proven to be successful in treating different disease types. Avastin can reportedly cost between $4,000-$9,000 a month, depending on a patient’s weight and the type of cancer.
Cost-effective options for cancer drugs
Increased demand for these treatments can drive up the costs. In such a landscape, medicine access programmes can help ensure more cost-effective options and efficient access to vital drugs for patients. Partnering with leading clinical trial services provider Oximio can assist patients and physicians in accessing effective oncology treatments such as Keytruda, Avastin, and Pomalidomide.
Through access programmes, Oximio can help patients obtain oncology drugs often at lower prices than on the market – potentially saving thousands of euros per treatment. For the use of these drugs in clinical trials, Oximio can quickly provide a quote.
With an extensive global network of depots and warehouses, Oximio provides a range of options such as worldwide shipping and adaptive logistics to ensure shorter supply routes and protect the contents of shipments. Oximio can also meet exact batch requirements with expiry dates for a specific patient or trial.
With almost two decades of expertise in clinical trial services, Oximio can assist with navigating regulatory obstacles in the client’s country to ensure patients receive vital medical treatments – often ahead of schedule.
To learn about the major trends in oncology clinical trials and Oximio’s specialist logistics services, download the document below.