Barriers and motivational factors for engaging in novel opioid and medication disposal-related services in community pharmacies.

OBJECTIVE: This study aimed to explore and identify motivational factors and barriers for pharmacy personnel participation in specific opioid mitigation programs, using the Theory of Planned Behavior (TPB) as an investigational framework. METHODS: A naturalistic inquiry method was employed involving semi-structured interviews with pharmacy personnel to assess their intentions, attitudes, normative beliefs, and behaviors towards participating in naloxone dispensing and provision of at-home drug disposal solutions. Purposive sampling was utilized to recruit participants, with saturation achieved after 12 interviews. Interviews were transcribed and coded to identify recurring themes. RESULTS: Four primary themes emerged: 1) the value and benefits of helping others, emphasizing societal, patient, and environmental benefits; 2) limits and barriers to participation, including financial concerns, management support, and time constraints; 3) pharmacists' intrinsic motivators, highlighting personal motivations and differentiation between programs for specific patient types; and 4) program implementation challenges and strategies. CONCLUSION: The findings underscore the applicability of the TPB in understanding pharmacy engagement in opioid abatement programs. Despite facing barriers such as financial considerations and time constraints, the overall positive attitudes towards the programs indicate a strong motivation to contribute to public health efforts. Addressing identified barriers and leveraging motivators could enhance participation, potentially mitigating the opioid crisis. Future research should incorporate patient perspectives to fully understand the impact and effectiveness of pharmacy-led interventions, such as naloxone dispensing and disposal solutions, in opioid misuse prevention.

Machine learning for skin permeability prediction: random forest and XG boost regression.

Background: Machine learning algorithms that can quickly and easily estimate skin permeability (Kp) are increasingly being used in drug delivery research. The linear free energy relationship (LFER) developed by Abraham is a practical technique for predicting Kp. The permeability coefficients and Abraham solute descriptor values for 175 organic compounds have been documented in the scientific literature.Purpose: The purpose of this project was to use a publicly available dataset to make skin permeability predictions using the random forest and XBoost regression techniques.Methods: We employed Pandas-based methods in JupyterLab to predict permeability coefficient (Kp) from solute descriptors (excess molar refraction [E], combined dipolarity/polarizability [S], overall solute hydrogen bond acidity and basicity [A and B], and the McGowan's characteristic molecular volume [V]).Results: The random forest and XG Boost regression models established statistically significant association between the descriptors and the skin permeability coefficient.