Applications of Advanced Natural Language Processing for Clinical Pharmacology

Remdesivir associated with reduced mortality in hospitalized COVID-19 patients: treatment effectiveness using real-world data and natural language processing

BACKGROUND

Remdesivir (RDV) was the first antiviral approved for mild-to-moderate COVID-19 and for those patients at risk for progression to severe disease after clinical trials supported its association with improved outcomes. Real-world evidence (RWE) generated by artificial intelligence techniques could potentially expedite the validation of new treatments in future health crises. We aimed to use natural language processing (NLP) and machine learning (ML) to assess the impact of RDV on COVID19-associated outcomes including time to discharge and in-hospital mortality.

METHODS

Using EHRead®, an NLP technology including SNOMED-CT terminology that extracts unstructured clinical information from electronic health records (EHR), we retrospectively examined hospitalized COVID-19 patients with moderate-to-severe pneumonia in three Spanish hospitals between January 2021 and March 2022. Among RDV eligible patients, treated (RDV+) vs untreated (RDV‒) patients were compared after propensity score matching (PSM; 1:3.3 ratio) based on age, sex, Charlson comorbidity index, COVID-19 vaccination status, other COVID-19 treatment, hospital, and variant period. Cox proportional hazards models and Kaplan-Meier plots were used to assess statistical differences between groups.

RESULTS

Among 7,651,773 EHRs from 84,408 patients, 6,756 patients were detected with moderate-to-severe COVID-19 pneumonia during the study period. The study population was defined with 4,882 (72.3%) RDV eligible patients. The median age was 72 years and 57.3% were male. A total of 812 (16.6%) patients were classified as RDV+ and were matched to 2,703 RDV‒ patients (from a total of 4,070 RDV‒). After PSM, all covariates had an absolute mean standardized difference of less than 10%. The hazard ratio for in-hospital mortality at 28 days was 0.73 (95% confidence interval, CI, 0.56 to 0.96, p = 0.022) with RDV‒ as the reference group. Risk difference and risk ratio at 28 days was 2.7% and 0.76, respectively, both favoring the RDV+ group. No differences were found in length of hospital stay since RDV eligibility between groups.

CONCLUSIONS

Using NLP and ML we were able to generate RWE on the effectiveness of RDV in COVID-19 patients, confirming the potential of using this methodology to measure the effectiveness of treatments in pandemics. Our results show that using RDV in hospitalized patients with moderate-to-severe pneumonia is associated with significantly reduced inpatient mortality. Adherence to clinical guideline recommendations has prognostic implications and emerging technologies in identifying eligible patients for treatment and avoiding missed opportunities during public health crises are needed.

Enhancing drug discovery and patient care through advanced analytics with the power of NLP and machine learning in pharmaceutical data interpretation

This study delves into the transformative potential of Machine Learning (ML) and Natural Language Processing (NLP) within the pharmaceutical industry, spotlighting their significant impact on enhancing medical research methodologies and optimizing healthcare service delivery. Utilizing a vast dataset sourced from a well-established online pharmacy, this research employs sophisticated ML algorithms and cutting-edge NLP techniques to critically analyze medical descriptions and optimize recommendation systems for drug prescriptions and patient care management. Key technological integrations include BERT embeddings, which provide nuanced contextual understanding of complex medical texts, and cosine similarity measures coupled with TF-IDF vectorization to significantly enhance the precision and reliability of text-based medical recommendations. By meticulously adjusting the cosine similarity thresholds from 0.2 to 0.5, our tailored models have consistently achieved a remarkable accuracy rate of 97 %, illustrating their effectiveness in predicting suitable medical treatments and interventions. These results not only highlight the revolutionary capabilities of NLP and ML in harnessing data-driven insights for healthcare but also lay a robust groundwork for future advancements in personalized medicine and bespoke treatment pathways. Comprehensive analysis demonstrates the scalability and adaptability of these technologies in real-world healthcare settings, potentially leading to substantial improvements in patient outcomes and operational efficiencies within the healthcare system.

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.