Applications of Advanced Natural Language Processing for Clinical Pharmacology

Dosage Optimization: A Regulatory Perspective for Developing Oncology Drugs

Optimized dosages provide a secure foundation for the development of well-tolerated and effective oncology drugs. Project Optimus, an initiative within the Oncology Center of Excellence, strives to reform the dosage optimization and dosage selection paradigm in oncology. This initiative stems from the availability of targeted drugs and from the demand for more tolerable dosages from patients, caregivers, and advocates. Reforming dosage optimization for oncology drugs requires a paradigm shift from the one employed for cytotoxic chemotherapy to one that understands and considers the unique attributes of targeted therapy, immunotherapy, and cellular therapy. Limited characterization of dosage during drug development may result in (1) patients not staying on a therapy long-term due to poor tolerability, (2) failure to establish positive benefit-risk in clinical trials for regulatory approval (3) withdrawal of drugs from the market (4) removal of indications of drugs from the market. Timely access to drugs is important for all patients with cancer, so it is vital to iteratively analyze all nonclinical and clinically relevant data at each stage of development and leverage quantitative approaches, innovative trial designs, and regulatory support to arrive at dosages with favorable benefit-risk. This review highlights the key challenges and opportunities to embracing this new paradigm and realizing the full potential of new oncology therapies.

Dose Optimization in Oncology Drug Development: An International Consortium for Innovation and Quality in Pharmaceutical Development White Paper

The landscape of oncology drug development has witnessed remarkable advancements over the last few decades, significantly improving clinical outcomes and quality of life for patients with cancer. Project Optimus, introduced by the U.S. Food and Drug Administration, stands as a groundbreaking endeavor to reform dose selection of oncology drugs, presenting both opportunities and challenges for the field. To address complex dose optimization challenges, an Oncology Dose Optimization IQ Working Group was created to characterize current practices, provide recommendations for improvement, develop a clinical toolkit, and engage Health Authorities. Historically, dose selection for cytotoxic chemotherapeutics has focused on the maximum tolerated dose, a paradigm that is less relevant for targeted therapies and new treatment modalities. A survey conducted by this group gathered insights from member companies regarding industry practices in oncology dose optimization. Given oncology drug development is a complex effort with multidimensional optimization and high failure rates due to lack of clinically relevant efficacy, this Working Group advocates for a case-by-case approach to inform the timing, specific quantitative targets, and strategies for dose optimization, depending on factors such as disease characteristics, patient population, mechanism of action, including associated resistance mechanisms, and therapeutic index. This white paper highlights the evolving nature of oncology dose optimization, the impact of Project Optimus, and the need for a tailored and evidence-based approach to optimize oncology drug dosing regimens effectively.

Consore: A Powerful Federated Data Mining Tool Driving a French Research Network to Accelerate Cancer Research

BACKGROUND

Real-world data (RWD) related to the health status and care of cancer patients reflect the ongoing medical practice, and their analysis yields essential real-world evidence. Advanced information technologies are vital for their collection, qualification, and reuse in research projects.

METHODS

UNICANCER, the French federation of comprehensive cancer centres, has innovated a unique research network: Consore. This potent federated tool enables the analysis of data from millions of cancer patients across eleven French hospitals.

RESULTS

Currently operational within eleven French cancer centres, Consore employs natural language processing to structure the therapeutic management data of approximately 1.3 million cancer patients. These data originate from their electronic medical records, encompassing about 65 million medical records. Thanks to the structured data, which are harmonized within a common data model, and its federated search tool, Consore can create patient cohorts based on patient or tumor characteristics, and treatment modalities. This ability to derive larger cohorts is particularly attractive when studying rare cancers.

CONCLUSIONS

Consore serves as a tremendous data mining instrument that propels French cancer centres into the big data era. With its federated technical architecture and unique shared data model, Consore facilitates compliance with regulations and acceleration of cancer research projects.