drug response data

Introduction

Innovations in Artificial Intelligence (AI) have propelled pharmaceutical companies to revolutionize their approaches to designing, testing, and bringing precision medicine and healthcare solutions to the market. Two key elements in advancing precision medicine include early disease detection and understanding drug responders within distinct populations. By leveraging genomics and clinical notes, AI companies, specifically in the TechBio space, are transforming the way biopharma industries identify, understand, and cater to individuals rather than whole populations.

The Challenge: Precision Medicine and Drug Response

Traditional drug development methods often analyze the success of a drug treatment as its effect on a patient population, leading to highly variable outcomes and adverse effects among individual patients. This is despite the fact that for many diseases the underlying mechanisms driving symptoms can be quite different from person to person. This lack of individualization in treatment can hinder therapeutic efficacy at the group level despite effectiveness for certain individuals. If we hope to accelerate drug development to get cures in the hands of people faster, future research needs intelligent, cost-effective methods to stratify patients based on the contribution of different disease mechanisms and drug processing capabilities. AI companies are helping biopharma address this challenge by incorporating genomics and insights garnered from those individual’s de-identified patient clinical charts in a systematic way.

Genomics: The Blueprint of Personalization

The genomic revolution has undoubtedly paved the way for precision medicine in Biopharma. By analyzing an individual’s genetic data, scientists can identify variations that may influence drug metabolism and response. This approach has already proven highly effective, particularly in the case of breast cancer patients. In some instances of breast cancer, there is an overexpression of the HER2/neu protein (Gutierrez & Schiff, 2011). When genomic markers for this overexpression are identified, anti-HER2 antibodies can be incorporated into the treatment regimen, significantly enhancing survival rates. AI companies are at the forefront of continuing this research by utilizing genomics for the creation of genetic sub-groups essential for biomarker discovery and predicting the most effective drug treatments for individual patients (Quazi, 2022).

Disease Detection and Monitoring with AI-Enhanced Biomarker Research

Early detection and monitoring of disease progression are paramount for improving patient survival rates. Traditionally, biomarker research has focused on identifying individual molecules or transcripts that can serve as early indicators of future severe illness. However, the field is evolving beyond the notion of a single-molecule biomarker diagnostic. Instead, it is turning to AI to examine the relationships between molecules and transcripts, offering a more comprehensive approach to identifying the onset of significant diseases (Vazquez-Levin et al., 2023). Over the past decade, cancer research and clinical decision-making have undergone a significant transformation, shifting from qualitative data to a wealth of quantitative digital information.

Universities and clinical institutions globally have contributed a vast trove of biomarkers and imaging data. This extensive dataset encompasses insights from genomics, proteomics, metabolomics, and various omics disciplines, as well as inputs from oncology clinics, epidemiology, and medical imaging. AI, uniquely positioned to integrate this diverse information, holds the potential to spearhead the development of pioneering predictive models for drug responses, paving the way for groundbreaking advancements in disease diagnosis, treatment prediction, and overall decision-making concerning novel therapies. 

With growing collections of data, it is becoming easier to model how a drug will shift an individual’s biology for worse or better. A recent example of this modelling is in the Cancer Patient Digital Twin (CPDT) project, where, the collection of multimodal temporal data from cancer patients can be employed to build a Digital Twin (a virtual replica of a patient’s biological processes and health status), allowing for in silico experimentation, which may guide testing, treatment, or decision points (Stahlberg et al., 2022).

One example is how the detection of metastatic disease over time could be improved from radiology reports. Researchers exposed prediction models to historical information using Natural Language Processing (NLP) (Batch et al., 2022). The authors were able to extract and encode relevant features from medical text reports, and use these features to develop, train, and validate models. Over 700 thousand radiology reports were used for model development to predict the presence of metastatic disease. Results from this study suggest that NLP models can extract cancer progression patterns from multiple consecutive reports and predict the presence of metastatic disease in multiple organs with higher performance than previous analytical techniques. Early knowledge of disease states or disease risk could lead to revised risk:benefit assessments for treatments and testing, potentially influencing patients’ choices. As a result, patients with otherwise comparable profiles may opt for treatments or tests they would not have otherwise considered. Even in cases where we do not have good biomarkers for disease (for example, Alzheimer’s disease, where most of the biomarkers are quite invasive to collect), knowing that a person has a higher disease risk earlier can enable important research that can lead to better biomarkers and, ultimately, better treatments.     

AI-Driven Pharmacogenomics: Revolutionizing Precision Medicine and Clinical Trials

While traditional approaches have paved the way for tailored medical treatments, the integration of AI can supercharge these efforts by leveraging an individual’s genetic information. For instance, consider the case of Warfarin, a widely prescribed anticoagulant. Accurate dosing for Warfarin is critical during the start of treatment, which carries higher risks of bleeding and clotting issues. Over decades, dose-response models have been developed to better understand how this drug affects the human body (Holford, 1986). To improve on Warfarin anticoagulation therapy, algorithms have incorporated genetic information to aid in identifying the factors behind clotting issues like Warfarin clearance rate, improving dosage and therapy (Gong et al., 2011). 

Now, with the power of AI, researchers can expedite the personalization of treatments for various disorders and medications, similar to what was accomplished with Warfarin but in a fraction of the time. AI algorithms are starting to analyze an individual’s genetic profile to predict their specific responses to various medications. This approach enables healthcare providers to fine-tune treatment plans, taking into account an individual’s unique genetic makeup, thus optimizing the effectiveness of therapies and reducing the potential for adverse effects. The integration of AI not only enhances the precision of pharmacogenomics but also streamlines the process, ultimately leading to safer and more efficient medical care tailored to each patient’s genetic characteristics.

The ultimate aspiration is to develop a sophisticated AI-driven system that can accurately forecast how each individual will react to specific medications, with the potential to bypass the conventional, time-consuming method of starting with the lowest effective dose and incrementally adjusting it upwards. This trial-and-error approach often leads to prolonged periods of uncertainty and potential adverse side effects for patients. Such advancements not only boost the precision of healthcare but also elevate the overall quality of life for patients seeking rapid relief and improved well-being.

Moreover, the integration of AI in pharmacogenomics has the potential to significantly expedite clinical trial programs. By tailoring medication doses to specific genetic backgrounds, AI aids at all three phases of the clinical trial process. This approach not only streamlines the trials but also offers substantial time and cost savings. The ability to tailor treatments for different genetic subgroups ensures that clinical trials are more efficient, bringing new therapies to market faster and ultimately benefiting patients in need.

Conclusion

The union of genomics and clinical notes, facilitated by AI, is ushering in a new era of precision medicine in biopharma. With the ability to predict individual drug responses and identify targeted therapies, this approach holds immense promise for improved treatment outcomes and a patient-centric view of medicine. As AI companies continue to advance their capabilities, the future of precision medicine for many diseases is looking closer than ever. The key to unlocking its full potential lies in the availability of high-quality data that comprehensively spans the entire patient journey. The integration of such diverse health-related data is central to driving valuable insights for drug development, making AI a driving force in the future of healthcare.

 

Citations:

Batch, K. E., Yue, J., Darcovich, A., Lupton, K., Liu, C. C., Woodlock, D. P., El Amine, M. A. K., Causa-Andrieu, P. I., Gazit, L., Nguyen, G. H., Zulkernine, F., Do, R. K. G., & Simpson, A. L. (2022). Developing a Cancer Digital Twin: Supervised Metastases Detection From Consecutive Structured Radiology Reports. Frontiers in Artificial Intelligence, 5. https://doi.org/10.3389/frai.2022.826402

Gong, I. Y., Schwarz, U. I., Crown, N., Dresser, G. K., Lazo-Langner, A., Zou, G., Roden, D. M., Stein, C. M., Rodger, M., Wells, P. S., Kim, R. B., & Tirona, R. G. (2011). Clinical and genetic determinants of warfarin pharmacokinetics and pharmacodynamics during treatment initiation. PloS One, 6(11), e27808. https://doi.org/10.1371/journal.pone.0027808

Gutierrez, C., & Schiff, R. (2011). HER2: biology, detection, and clinical implications. Archives of Pathology & Laboratory Medicine, 135(1), 55–62. https://doi.org/10.5858/2010-0454-RAR.1

Holford, N. H. (1986). Clinical pharmacokinetics and pharmacodynamics of warfarin. Understanding the dose-effect relationship. Clinical Pharmacokinetics, 11(6), 483–504. https://doi.org/10.2165/00003088-198611060-00005

Quazi, S. (2022). Artificial intelligence and machine learning in precision and genomic medicine. Medical Oncology (Northwood, London, England), 39(8), 120. https://doi.org/10.1007/s12032-022-01711-1

Stahlberg, E. A., Abdel-Rahman, M., Aguilar, B., Asadpoure, A., Beckman, R. A., Borkon, L. L., Bryan, J. N., Cebulla, C. M., Chang, Y. H., Chatterjee, A., Deng, J., Dolatshahi, S., Gevaert, O., Greenspan, E. J., Hao, W., Hernandez-Boussard, T., Jackson, P. R., Kuijjer, M., Lee, A., … Zervantonakis, I. (2022). Exploring approaches for predictive cancer patient digital twins: Opportunities for collaboration and innovation. Frontiers in Digital Health, 4, 1007784. https://doi.org/10.3389/fdgth.2022.1007784

Vazquez-Levin, M. H., Reventos, J., & Zaki, G. (2023). Editorial: Artificial intelligence: A step forward in biomarker discovery and integration towards improved cancer diagnosis and treatment. Frontiers in Oncology, 13, 1161118. https://doi.org/10.3389/fonc.2023.1161118

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