Clinical Management of Patients with Non-Small Cell Lung Cancer, Brain Metastases, and Actionable Genomic Alterations: A Systematic Literature Review.

INTRODUCTION: Nearly 60% of patients with non-small cell lung cancer (NSCLC) present with metastatic disease, and approximately 20% have brain metastases (BrMs) at diagnosis. During the disease course, 25-50% of patients will develop BrMs. Despite available treatments, survival rates for patients with NSCLC and BrMs remain low, and their overall prognosis is poor. Even with newer agents for NSCLC, options for treating BrMs can be limited by their ineffective transport across the blood-brain barrier (BBB) and the unique brain tumor microenvironment. The presence of actionable genomic alterations (AGAs) is a key determinant of optimal treatment selection, which aims to maximize responses and minimize toxicities. The objective of this systematic literature review (SLR) was to understand the current landscape of the clinical management of patients with NSCLC and BrMs, particularly those with AGAs. METHOD: A Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA)-compliant SLR was conducted to identify studies in patients with BrMs in NSCLC. Searches used the EMBASE and MEDLINE® databases, and articles published between January 1, 2017 and September 26, 2022 were reviewed. RESULTS: Overall, 179 studies were included in the SLR. This subset review focused on 80 studies that included patients with NSCLC, BrMs, and AGAs (19 randomized controlled trials [RCTs], two single-arm studies, and 59 observational studies). Sixty-four of the 80 studies reported on epidermal growth factor receptor (EGFR) mutations, 14 on anaplastic lymphoma kinase (ALK) alterations, and two on both alterations. Ninety-five percent of studies evaluated targeted therapy. All RCTs allowed patients with previously treated, asymptomatic, or neurologically stable BrMs; the percentage of asymptomatic BrMs varied across observational studies. CONCLUSIONS: Although targeted therapies demonstrate systemic benefits for patients with NSCLC, BrMs, and AGAs, there remains a continued need for effective therapies to treat and prevent BrMs in this population. Increased BBB permeability of emerging therapies may improve outcomes for this population.

Potential biomarkers in breast cancer drug development: application of the biomarker qualification evidentiary framework.

Breast cancer treatments have evolved rapidly, and clinically meaningful biomarkers have been used to guide therapy. These biomarkers hold utility within the drug development process to increase the efficiency and effectiveness. To this purpose, the US FDA developed an evidentiary framework. Literature searches conducted of literature published between 2016 and 2022 identified biomarkers in breast cancer. These biomarkers were reviewed for drug development utility through the biomarker qualification evidentiary framework. In the breast cancer setting, several promising biomarkers (ctDNA, Ki-67 and PIK3CA) were identified. There is a need for increased transparency regarding the requirements for qualification of specific biomarkers and increased awareness of the processes involved in biomarker qualification.

The Use of Biomarkers in Pharmacovigilance: A Systematic Review of the Literature.

Background: The use of biomarkers varies from disease etiognosis and diagnosis to signal detection, risk prediction, and management. Biomarker use has expanded in recent years, however, there are limited reviews on the use of biomarkers in pharmacovigilance and specifically in the monitoring and management of adverse drug reactions (ADRs). Objective: The objective of this manuscript is to identify the multiple uses of biomarkers in pharmacovigilance irrespective of the therapeutic area. Design: This is a systematic review of the literature. Data Sources and Methods: Embase and MEDLINE database searches were conducted for literature published between 2010-March 19, 2021. Scientific articles that described the potential use of biomarkers in pharmacovigilance in sufficient detail were reviewed. Papers that did not fulfill the United States Food and Drug Administration (US FDA) definition of a biomarker were excluded, which is based on the International Conference on Harmonisation (ICH)-E16 guidance. Results: Twenty-seven articles were identified for evaluation. Most articles involved predictive biomarkers (41%), followed by safety biomarkers (38%), pharmacodynamic/response biomarkers (14%), and diagnostic biomarkers (7%). Some articles described biomarkers that applied to multiple categories. Conclusion: Various categories of biomarkers including safety, predictive, pharmacodynamic/response, and diagnostic biomarkers are being investigated for potential use in pharmacovigilance. The most frequent potential uses of biomarkers in pharmacovigilance in the literature were the prediction of the severity of an ADR, mortality, response, safety, and toxicity. The safety biomarkers identified were used to evaluate patient safety during dose escalation, identify patients who may benefit from further biomarker testing during treatment, and monitor ADRs.

The Use of Artificial Intelligence in Pharmacovigilance: A Systematic Review of the Literature.

INTRODUCTION: Artificial intelligence through machine learning uses algorithms and prior learnings to make predictions. Recently, there has been interest to include more artificial intelligence in pharmacovigilance of products already in the market and pharmaceuticals in development. OBJECTIVE: The aim of this study was to identify and describe the uses of artificial intelligence in pharmacovigilance through a systematic literature review. METHODS: Embase and MEDLINE database searches were conducted for articles published from January 1, 2015 to July 9, 2021 using search terms such as 'pharmacovigilance,' 'patient safety,' 'artificial intelligence,' and 'machine learning' in the title or abstract. Scientific articles that contained information on the use of artificial intelligence in all modalities of patient safety or pharmacovigilance were reviewed and synthesized using a pre-specified data extraction template. Articles with incomplete information and letters to editor, notes, and commentaries were excluded. RESULTS: Sixty-six articles were identified for evaluation. Most relevant articles on artificial intelligence focused on machine learning, and it was used in patient safety in the identification of adverse drug events (ADEs) and adverse drug reactions (ADRs) (57.6%), processing safety reports (21.2%), extraction of drug-drug interactions (7.6%), identification of populations at high risk for drug toxicity or guidance for personalized care (7.6%), prediction of side effects (3.0%), simulation of clinical trials (1.5%), and integration of prediction uncertainties into diagnostic classifiers to increase patient safety (1.5%). Artificial intelligence has been used to identify safety signals through automated processes and training with machine learning models; however, the findings may not be generalizable given that there were different types of data included in each source. CONCLUSION: Artificial intelligence allows for the processing and analysis of large amounts of data and can be applied to various disease states. The automation and machine learning models can optimize pharmacovigilance processes and provide a more efficient way to analyze information relevant to safety, although more research is needed to identify if this optimization has an impact on the quality of safety analyses. It is expected that its use will increase in the near future, particularly with its role in the prediction of side effects and ADRs.