AIMC Topic: Drug Development

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Advancing precision cancer immunotherapy drug development, administration, and response prediction with AI-enabled Raman spectroscopy.

Frontiers in immunology
Molecular characterization of tumors is essential to identify predictive biomarkers that inform treatment decisions and improve precision immunotherapy development and administration. However, challenges such as the heterogeneity of tumors and patien...

AI-directed formulation strategy design initiates rational drug development.

Journal of controlled release : official journal of the Controlled Release Society
Rational drug development would be impossible without selecting the appropriate formulation route. However, pharmaceutical scientists often rely on limited personal experiences to perform trial-and-error tests on diverse formulation strategies. Such ...

Progress, Pitfalls, and Impact of AI-Driven Clinical Trials.

Clinical pharmacology and therapeutics
Since the deep learning revolution of the early 2010s, significant efforts and billions of dollars have been invested in applying artificial intelligence (AI) to drug discovery and development (AIDD). However, despite high expectations, few AI-discov...

Generative AI: driving productivity and scientific breakthroughs in pharmaceutical R&D.

Drug discovery today
The rapid advancement of generative artificial intelligence (AI) is reshaping pharmaceutical research and development (R&D), offering opportunities across drug discovery and development. Generative AI (GenAI) enhances productivity by enabling virtual...

FormulationBCS: A Machine Learning Platform Based on Diverse Molecular Representations for Biopharmaceutical Classification System (BCS) Class Prediction.

Molecular pharmaceutics
The Biopharmaceutics Classification System (BCS) has facilitated biowaivers and played a significant role in enhancing drug regulation and development efficiency. However, the productivity of measuring the key discriminative properties of BCS, solubi...

Accelerating virtual patient generation with a Bayesian optimization and machine learning surrogate model.

CPT: pharmacometrics & systems pharmacology
The pharmaceutical industry has increasingly adopted model-informed drug discovery and development (MID3) to enhance productivity in drug discovery and development. Quantitative systems pharmacology (QSP), which integrates drug action mechanisms and ...

The Role of Artificial Intelligence and Machine Learning in Accelerating the Discovery and Development of Nanomedicine.

Pharmaceutical research
The unique potential of nanomedicine to address challenging health issues is rapidly advancing the field, leading to the generation of more effective products. However, these complex systems often pose several challenges with respect to their design ...

Strategic partnerships for AI-driven drug discovery: The role of relational dynamics.

Drug discovery today
Artificial intelligence-driven drug discovery (AIDD) companies hold significant promise for transforming pharmaceutical development, yet little is known about how they manage partnerships with established pharmaceutical firms. To address this researc...

Prediction of Multi-Pharmacokinetics Property in Multi-Species: Bayesian Neural Network Stacking Model with Uncertainty.

Molecular pharmaceutics
Pharmacokinetic (PK) properties of a drug are vital attributes influencing its therapeutic effectiveness, playing an important role in the drug development process. Focusing on the difficult task of predicting PK parameters, we compiled an extensive ...

Machine learning driven bioequivalence risk assessment at an early stage of generic drug development.

European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V
BACKGROUND: Bioequivalence risk assessment as an extension of quality risk management lacks examples of quantitative approaches to risk assessment at an early stage of generic drug development. The aim of our study was to develop a model-based approa...