Study design and methods for the using exercise to relieve joint pain and improve AI adherence in older breast cancer survivors (REJOIN) trial

A comprehensive review of neurotransmitter modulation via artificial intelligence: A new frontier in personalized neurobiochemistry

The deployment of artificial intelligence (AI) is revolutionizing neuropharmacology and drug development, allowing the modulation of neurotransmitter systems at the personal level. This review focuses on the neuropharmacology and regulation of neurotransmitters using predictive modeling, closed-loop neuromodulation, and precision drug design. The fusion of AI with applications such as machine learning, deep-learning, and even computational modeling allows for the real-time tracking and enhancement of biological processes within the body. An exemplary application of AI is the use of DeepMind's AlphaFold to design new GABA reuptake inhibitors for epilepsy and anxiety. Likewise, Benevolent AI and IBM Watson have fast-tracked drug repositioning for neurodegenerative conditions. Furthermore, we identified new serotonin reuptake inhibitors for depression through AI screening. In addition, the application of Deep Brain Stimulation (DBS) settings using AI for patients with Parkinson's disease and for patients with major depressive disorder (MDD) using reinforcement learning-based transcranial magnetic stimulation (TMS) leads to better treatment. This review highlights other challenges including algorithm bias, ethical concerns, and limited clinical validation. Their proposal to incorporate AI with optogenetics, CRISPR, neuroprosthesis, and other advanced technologies fosters further exploration and refinement of precision neurotherapeutic approaches. By bridging computational neuroscience with clinical applications, AI has the potential to revolutionize neuropharmacology and improve patient-specific treatment strategies. We addressed critical challenges, such as algorithmic bias and ethical concerns, by proposing bias auditing, diverse datasets, explainable AI, and regulatory frameworks as practical solutions to ensure equitable and transparent AI applications in neurotransmitter modulation.

"Interventions to increase adherence to oral therapies in breast cancer patients: A systematic review based on the behavior change technique taxonomy"

OBJECTIVE

High rates of non-adherence to oral medications in breast cancer (BC) patients have been reported. Here we provide an up-to-date systematic review of the interventions aimed at increasing adherence to oral medication in BC patients, with a particular focus on the content of the interventions.

METHODS

PubMed, Scopus, Embase and Ovid databases and reference lists of relevant studies were searched through October 2022. Studies which (1) described an intervention aimed at increasing adherence to oral anticancer medication, (2) included (or planned to include) at least one sub-group of BC patients, (3) were written in English, and (4) with full-text available were included. The contents of the interventions were coded using the Behavior Change Technique Taxonomy. Quality assessment was conducted using Downs and Black scale.

RESULTS

Thirty-six studies met the inclusion criteria and involved a total sample of 28,528 BC patients. Interventions were mainly delivered with eHealth devices (n = 21) and most of them used mobile app. Other studies used in-person modalities (e.g., CBT, relaxation technique) or written materials (e.g., psycho-educational booklet). The behavior change techniques most frequently implemented were "problem solving," "social support," "information about health consequences," and "prompts/cues". Quality assessment revealed that the higher risk of bias refers to the selection process.

CONCLUSIONS

The use of reminders, monitoring patients' medication-taking behaviors and giving feedback were the most frequently implemented techniques in those interventions that resulted significant. If these preliminary observations were to be confirmed by future comparative studies, they should be taken into account when developing new interventions.

Adapting an Evidence-Based Exercise and Education Program for Older Breast Cancer Survivors for the REJOIN Trial

Physical activity (PA) promotes survival and mitigates symptoms in older breast cancer survivors (BCS), especially to reduce joint pain associated with adjuvant hormonal treatment. The purpose is to describe the adaptation process for an evidence-based exercise and education curriculum (i.e., Fit & Strong!) to support older BCS participating in the Using Exercise to Relieve Joint Pain and Improve Aromatase Inhibitor Adherence in Older Breast Cancer Survivors trial. We reviewed all educational materials with scientific/clinical experts to identify necessary content changes. Next, we conducted semistructured phone interviews with BCS to review all educational materials and conducted a real-time pretest for the trial. Overall, BCS found the adapted materials and experience acceptable (mean score of 9.2/10 for satisfaction). Content changes included simplifying exercise instructions, prioritizing content related to the trial goals, and updating photographs. Because of COVID, the pretest was conducted via Zoom. Our multistep adaptation process provided an acceptable intervention to meet the needs of older BCS. Lessons learned will be applied to the forthcoming pilot trial.