Predicting optimal treatment regimens for patients with HR+/HER2- breast cancer using machine learning based on electronic health records

Artificial Intelligence for Quantitative Modeling in Drug Discovery and Development: An Innovation and Quality Consortium Perspective on Use Cases and Best Practices

Recent breakthroughs in artificial intelligence (AI) and machine learning (ML) have ushered in a new era of possibilities across various scientific domains. One area where these advancements hold significant promise is model-informed drug discovery and development (MID3). To foster a wider adoption and acceptance of these advanced algorithms, the Innovation and Quality (IQ) Consortium initiated the AI/ML working group in 2021 with the aim of promoting their acceptance among the broader scientific community as well as by regulatory agencies. By drawing insights from workshops organized by the working group and attended by key stakeholders across the biopharma industry, academia, and regulatory agencies, this white paper provides a perspective from the IQ Consortium. The range of applications covered in this white paper encompass the following thematic topics: (i) AI/ML-enabled Analytics for Pharmacometrics and Quantitative Systems Pharmacology (QSP) Workflows; (ii) Explainable Artificial Intelligence and its Applications in Disease Progression Modeling; (iii) Natural Language Processing (NLP) in Quantitative Pharmacology Modeling; and (iv) AI/ML Utilization in Drug Discovery. Additionally, the paper offers a set of best practices to ensure an effective and responsible use of AI, including considering the context of use, explainability and generalizability of models, and having human-in-the-loop. We believe that embracing the transformative power of AI in quantitative modeling while adopting a set of good practices can unlock new opportunities for innovation, increase efficiency, and ultimately bring benefits to patients.

Incorporation of emergent symptoms and genetic covariates improves prediction of aromatase inhibitor therapy discontinuation

Objectives

Early discontinuation is common among breast cancer patients taking aromatase inhibitors (AIs). Although several predictors have been identified, it is unclear how to simultaneously consider multiple risk factors for an individual. We sought to develop a tool for prediction of AI discontinuation and to explore how predictive value of risk factors changes with time.

Materials and Methods

Survival machine learning was used to predict time-to-discontinuation of AIs in 181 women who enrolled in a prospective cohort. Models were evaluated via time-dependent area under the curve (AUC), c-index, and integrated Brier score. Feature importance was analysis was conducted via Shapley Additive Explanations (SHAP) and time-dependence of their predictive value was analyzed by time-dependent AUC. Personalized survival curves were constructed for risk communication.

Results

The best-performing model incorporated genetic risk factors and changes in patient-reported outcomes, achieving mean time-dependent AUC of 0.66, and AUC of 0.72 and 0.67 at 6- and 12-month cutoffs, respectively. The most significant features included variants in ESR1 and emergent symptoms. Predictive value of genetic risk factors was highest in the first year of treatment. Decrease in physical function was the strongest independent predictor at follow-up.

Discussion and Conclusion

Incorporation of genomic and 3-month follow-up data improved the ability of the models to identify the individuals at risk of AI discontinuation. Genetic risk factors were particularly important for predicting early discontinuers. This study provides insight into the complex nature of AI discontinuation and highlights the importance of incorporating genetic risk factors and emergent symptoms into prediction models.

Place in Therapy of Cyclin-Dependent Kinase 4/6 Inhibitors in Breast Cancer: A Targeted Literature Review

Cyclin-dependent kinase 4 and 6 inhibitors (CDK4/6i) are the preferred regimen for patients with hormone receptor-positive and human epidermal growth factor receptor 2-negative (HR+/HER2-) advanced or metastatic breast cancer. However, the optimal treatment sequencing for CDK4/6i with other available therapeutic options is unclear. We conducted a targeted literature review to identify the current evidence on CDK4/6i treatment patterns in patients with breast cancer. The search was initially conducted in October 2021 and subsequently updated in October 2022. Biomedical databases and gray literature were searched, and bibliographies of included reviews were screened for relevant studies. The search identified ten reviews published since 2021 and 87 clinical trials or observational studies published since 2015. The included reviews discussed CDK4/6i usage with or without endocrine therapy (ET) in first-line and second-line treatment for patients with HR+/HER2- advanced or metastatic breast cancer, followed by ET, chemotherapy, or targeted therapy with ET. Clinical studies reported similar treatment sequences consisting of ET, chemotherapy, or targeted therapy with ET prior to CDK4/6i with ET, followed by ET monotherapy, chemotherapy, targeted therapy with ET, or continued CDK4/6i with ET. Current evidence suggests CDK4/6i are effective for HR+/HER2- advanced or metastatic breast cancer in earlier lines of therapy. Efficacy of CDK4/6i as measured by progression-free survival and overall survival was similar within a line of therapy regardless of the type of prior therapy. Survival on different post-CDK4/6i treatments was also similar within the same line of therapy. Additional research is needed to investigate the optimal place in therapy of CDK4/6i and the sequencing of treatments following progression on CDK4/6i.