Neurological disorders pose a profound clinical and scientific challenge due to their complex pathophysiology and inter-individual variability. Conventional drug development approaches frequently fail to address the multifactorial nature of these disorders and the intricacies of central nervous system (CNS) function. Emerging insights into the roles of astrocytes, synaptic architecture, and microglial dynamics underscore the need for precision strategies in both therapeutic design and delivery.

Recent advances in artificial intelligence (AI) and micro/nanotechnologies have catalyzed a paradigm shift in neuropharmacology. Microfluidics and microphysiological systems (e.g., brain-on-chip platforms) now enable high-resolution modelling of the CNS, providing biomimetic and dynamically controlled environments. These systems facilitate real-time monitoring of drug responses and allow integration with AI algorithms for enhanced experimental precision.

AI methods, particularly machine learning and deep learning, are increasingly used to decode complex neurobiological datasets, predict pharmacokinetic/pharmacodynamic (PK/PD) profiles, and identify novel drug targets. Furthermore, AI aids in optimizing drug screening, repurposing efforts, and the automated control of in vitro platforms, thereby reducing experimental variability and improving translatability.
Nanotechnology, especially in the development of advanced nanocarriers, complements these efforts by enabling site-specific drug delivery across the blood-brain barrier, enhancing bioavailability, and minimizing off-target effects.

The convergence of AI and nanotechnology holds the promise of personalized neurotherapeutics, offering data-driven, patient-tailored interventions based on predictive modelling and targeted delivery systems.
This collection aims to highlight the transformative potential of AI and nanotechnology in the field of drug discovery and personalized treatment, focusing on neurological disorders. The following topics will be explored:

AI Integration in Drug Discovery

- Machine Learning Models: To accurately predict pharmacokinetic and pharmacodynamic readouts in neurological conditions, while emphasizing the importance of robust biological evidence to validate these predictions and enhance drug discovery and development processes.
- Developing Predictive AI Models for Non-human preclinical Responses: Creating predictive AI automated models that forecast objective non-human (e.g. rodent) responses in physiological and disease states and to neuropharmacological treatments for translation to clinic.
- AI-driven Identification of Novel Therapeutic Targets: Employing AI technologies to search, visualise and discover novel therapeutic targets in neurological disorders, enabling the development of more effective and targeted treatments.
- Improvement of Drug Screening and Repurposing Through AI Algorithms: Enhancing the efficiency and effectiveness of drug screening and repurposing using AI algorithms, with a focus on identifying potential neuroactive compounds and on biological evidence to validate these findings.
- AI-assisted Drug discovery in vitro platforms: Applying machine learning algorithms to optimize the design of microfluidic channels, chambers, and gradients for drug delivery, cell culture, and real-time monitoring of drug delivery assays in 3D platforms (organ-on-chip).
- AI-based systems: Automated feedback control of overall platforms such as microfluidics or/and organ-on-chip enabling quantitative assessment of drug effects on in vitro biological models and qualitative experimental control.

Personalized Treatment Strategies

- Application of AI in Patient Stratification: Applying AI to stratify patients, allowing for tailored neuropharmacological interventions that meet individual needs and conditions.
- Developing Predictive AI Models for Human Responses: Creating predictive AI automated models that forecast objective human responses in physiological and disease states and to neuropharmacological treatments, with the goal of optimizing therapeutic outcomes for each individual.

Nanotechnology in Therapeutics

- Innovations in Nanoscale Drug Delivery Systems: Developing innovative nanoscale drug delivery systems that provide targeted and efficient therapeutic interventions for neurological disorders.
- Exploring Synergistic AI and Nanotechnology Effects: Investigating the synergistic effects of combining AI and nanotechnology to optimize the delivery and efficacy of neurological treatments.

Are you interested in contributing a manuscript to this Research Topic?

Artificial Intelligence and Micro/Nanotechnologies in Neuropharmacology: Accelerating Drug Screening and Personalized Treatments