The intersection between technology and health care is now more important than ever. At the SUM Group, we have always been believers in the power of innovation to resolve complex problems. This belief is evident in our latest foray into precision oncology medicine. We aim to revolutionize the treatment of cancer by developing a cutting edge research software platform that provides more targeted, personalized and effective therapeutic options.
We have developed a platform by combining expertise in AI and software engineering with clinical and biological research. This not only improves cancer research, but empowers clinicians so they can make informed decisions. The platform will help identify the best drug treatment for each patient based on his or her unique tumor profile, ultimately leading them to better outcomes and lower costs.
In this article, we will explore how SUM Group approached the development of this innovative software solution, the technology behind it, and the profound impact it can have on the future of cancer treatment.
1. Tackling the Challenge of Personalized Cancer Treatment
Cancer is among the hardest conditions to cure. The algorithms have been trained to detect subtle patterns within the data, which would be extremely difficult, if certainly possible, for us to discern. This is partly because most treatment plans are generalized and fail to deliver. Each and every patient’s cancer is unique. Different aspects like genetics, tumor environments, and cancer pathways contribute to this. This individuality makes the “one-size-fits-all” approach to cancer treatment ineffective, often leading to unnecessary side effects and compromised patient outcomes.
At SUM Group, we saw a critical opportunity to leverage technology to personalize cancer treatment by using data to tailor drug regimens specifically to each patient’s tumor. Personalized medicine is a rapidly growing field aimed at customizing treatments based on an individual’s genetic and molecular profile. However, precision oncology requires an accurate, fast, and effective method to identify the best drugs and dosages for each patient. To try and solve this problem, we came up with an AI-enabled integrated biological research and predictive analytics platform.
2. A Revolutionary Approach: Combining AI and Biological Testing
As the only platform which combines AI and ex vivo biological testing, we derive most of our value and our “secret sauce” from our AI tool, Optim.AI™. Optim.AI™ leverages ex vivo testing on live tumor cells to predict the most effective treatment for each cancer patient. Because we are able to replicate real-world clinical scenarios to see how tumor cells respond to a treatment, we can predict clinical outcomes much more accurately compared to clinical outcomes predicted in a traditional lab setting. Our platform uses small data AI to make accurate predictions on limited tumor samples and the patient’s response to various drug combinations. The AI model will track and learn real-time data to predict outcomes as more and more experiments are completed on the patient and additional patient data is provided.
2.1 The Role of Artificial Intelligence in Precision Oncology
AI is changing many industries, including healthcare. In cancer research, AI analyzes large sets of data to find and predict treatment patterns and predict treatment results. For example, analyzing data from drug screening tests allows machine learning to predict how certain types of tumors will respond to different drugs. Analyzing historical data patterns allows algorithms to recommend promising drug combinations.
Using supervised learning, AI model training uses historical clinical data along with research results. The research platform AI continues enhancing its predictions and accuracy as new data from research is completed. The AI develops dynamic learning to cancer research at the AI model.
2.2 Ex Vivo Drug Sensitivity Testing
Ex vivo testing models provide AI with real biological data they can rely on. This approach involves testing drug combinations on live cancer cells harvested from patients. Tumor cells can then be cultured outside their bodies so as to be exposed to therapeutic agents in an controlled environment; this provides critical insight into how tumors react when exposed to various therapeutic agents compared with using only theoretical or in vitro models that don’t fully replicate human tumors.
By considering the results of drug sensitivity tests, this platform can create a tailored treatment plan based on each person’s tumor profile – marking a major breakthrough beyond conventional cancer care protocols that use generic therapies.
3. Key Technologies Behind the Software Development
Development of SUM Group’s advanced cancer research platform required expertise across several key technologies for its creation of a robust, scalable, and effective cancer solution.
3.1 Artificial Intelligence and Machine Learning
AI models at the center of our platform play an invaluable role in its ability to accurately forecast treatment outcomes. By analyzing ex vivo drug testing data and applying deep learning algorithms to it, they identify correlations between tumor characteristics and treatment responses – providing accurate predictions even with limited data available to us. This technology gives the platform the power to make highly precise predictions despite limited available information. We employed neural networks and ensemble methods, which are particularly effective for analyzing complex datasets with many variables. The algorithms have been trained to detect subtle patterns within the data, which would be hard, if possible, for us to recognize.
3.2 Cloud-Based Infrastructure
Due to the vast amount of data in cancer research, we realized that storage on the cloud would be inevitable to establish a flexible and scale-out platform. In using cloud services like AWS and Microsoft Azure, our large datasets are efficiently stored and processed securely. Besides, strong data analytics from the platform enabled the researchers to make sense of the outcome of the drug sensitivity tests. The cloud infrastructure also allows for real-time collaboration among researchers, clinicians, and biotech companies. The platform is accessible from anywhere in the world, enabling global collaboration on cutting-edge cancer research.
3.3 Data Analytics and Visualization Tools
The Python, R, as well as Matlab, enabled us to design customized analytics workflows, which helped the researchers visualize the trend and barcodes of the drug response, biomarkers identification, and treatment protocol optimization. We at SUM Group, we have always been motivated by an enthusiasm for innovation and an ardent desire to better the quality of life through technological advancements.
4. Real-World Impact and Clinical Validation
The ultimate goal of any research software in healthcare is to improve patient outcomes, and we are proud to say that our platform has been clinically validated through several pilot studies. The results have been promising, with our platform demonstrating a high level of accuracy in predicting treatment responses for patients with a range of cancers, including non-Hodgkin lymphoma.
Our platform can massively decrease trial-and-error in treatment by effectively forecasting what works in cancer treatment. It is presumed to save months, decrease expenditures and convert the QoL for the patient remarkably. In addition, the ability of the platform to identify efficient pair drugs is particularly crucial since solo drugs are rendered ineffective.
5. The Future of Precision Medicine in Cancer Treatment
In the future, we expect our platform to help direct cancer therapies. The platform will help predict personalized therapies more effectively as we gather more patient and clinical trial data.
In the continuously advancing field of cancer care, we hope to augment the platform to include other forms of personalized medicine, particularly genomic-based and immune-oncology therapies.
6. Conclusion
We at SUM Group, we have always been motivated by an enthusiasm for innovation and an ardent desire to better the quality of life through technological advancements. Our development of a research software platform for precision oncology is just one example of how software engineering and artificial intelligence can be used to solve some of healthcare’s most pressing challenges.
Through our work, we are proud to play a role in advancing cancer research and helping clinicians provide more effective, personalized treatment options for patients around the world. The journey doesn’t stop here, and as we continue to evolve and expand the platform, we look forward to contributing to the next generation of cancer care.