New Approaches to Machine Learning Parameters in Pharmacology and Pharmacotherapy for Drug Targeting and Precision Medicine

Topic Information

Dear Colleagues,

In medicine and pharmacotherapy, the practical use of AI tools and AI-derived information may be limited due to its involvement in critical, life-saving, or life-threatening decisions, which may affect, among other things, drug therapies and public health. In this context, AI should be considered as a supportive or advisory tool, and its outputs should be evaluated and verified using professional knowledge and experience, while ensuring the security, traceability, and auditability of AI-generated recommendations. In this project, we aim to highlight the need for a broader and deeper understanding of drug selection and use within the framework of precision and personalized therapy, using new strategies and highlighting emerging issues related to drug parameters to be used in machine learning. This includes new concepts and factors influencing drug action, risk–benefit assessment, dosing regimens, and other pharmacological parameters. Such approaches may contribute to improved drug design and targeting, as well as to safer and more effective drug use, ultimately increasing precision within personalized medicine. A further aim of this Topic is to identify limitations, misconceptions, external influences, drawbacks, and other effects related to the use of AI, including risks associated with data quality and model manipulation, which may also affect drug activity and therapeutic outcomes in medicine.

The subjects covered may include, but are not limited to, the following:

• Machine learning approaches in pharmacology and pharmacotherapy.
• Parameters for machine learning-based support in drug selection and drug targeting.
• Parameters for AI in clinical decision support (advisory role).
• Machine learning parameters in dose optimisation and posology.
• AI-assisted interpretation of pharmacokinetic and pharmacodynamic data.
• Machine learning parameters for AI prediction and assessment of drug interactions.
• Machine learning models for risk–benefit evaluation of therapies.
• AI in the interpretation of laboratory and biomarker data relevant to drug response.
• Machine learning parameters for AI applications in precision and personalised medicine.
• Limitations and uncertainties of AI in pharmacological decision-making.
• Bias, data quality, and reproducibility issues in AI-driven models.
• Ethical, clinical, and regulatory considerations of AI use in drug therapy.
• Pharmacological data governance and machine learning.
• AI model robustness and trustworthiness in pharmacology.

Prof. Dr. George J. Kontoghiorghes
Dr. Malamati Kourti
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Future Pharmacology futurepharmacol	2.7	-	2021	17.7 Days	CHF 1200	Submit
International Journal of Molecular Sciences ijms	5.6	10.0	2000	17.5 Days	CHF 2900	Submit
Pharmaceuticals pharmaceuticals	5.7	9.0	2004	14.3 Days	CHF 2900	Submit
Pharmaceutics pharmaceutics	6.9	12.5	2009	16.3 Days	CHF 2900	Submit

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