Artificial Intelligence and Predictive Models in Pharmaceutical Technologies

Convergence of drug delivery with artificial intelligence, machine learning, and mathematical modeling (PK / PD modeling, compartment modeling); development of AI/ML tools for materials selection and optimization of the pharmacological and biological performance of nanomedicines and delivery systems; modeling of biophysical processes (cell uptake, mass transport, biodistribution, biodegradation etc.) to optimize the performance of drug delivery systems.

Poster #	First Name	Last Name	Abstract Title	Abstract
113	Khair	Alhareth	Microfluidics, automation, and artificial intelligence for lipid nanoparticle design and production	View
114	Saad	Alshahrani	Leveraging Artificial Intelligence for Advancements in Pharmaceutical Research.	View
115	Ana	Melero Zaera	Computational simulations to predict the mirtazapine drug release kinetics from bentonite	View
116	Yuri	Dancik	Predicting the impact of differences in suppository formulations and in vitro dissolution methods on rectal drug pharmacokinetics using PBPK modelling	View
117	Branka	Grujić	An attempt of predicting printability of filaments using machine learning technique.	View
118	Remo	Eugster	Leveraging artificial intelligence to streamline the development of liposomal drug delivery systems	View
120	Joanne	Heade	AI-Driven Histomorphological Evaluation of Excipient Impact on Intestinal Tissue	View
122	Oleg	Igoshin	Uncovering the Interleukin-12 Pharmacokinetic Desensitization Mechanism and Its Consequences with Mathematical Modeling	View
123	Harshad	Jadhav	Accounting for colon absorption in physiologically based biopharmaceutics modeling of extended-release formulations	View
124	Harshad	Jadhav	Do physiologically relevant inputs improve drug absorption predictions in physiologically based biopharmaceutics modeling?	View
126	Robert	Kirian	Machine learning to predict extracellular vesicle properties for drug delivery applications	View
129	Thomas	Moore	MicrofluidicNP: A machine-learning platform for the microfluidic formulation development of nanomedicines	View
131	Teresa	Musumeci	Exploiting Machine learning technology to correlate mean size and zeta potential values of nanocarriers with drug targeting efficiency to the brain after intranasal administration.	View
132	Magdalini	Panagiotakopoulou	Machine Learning-Driven Approaches for Developing Targeted Lipid Nanoparticles	View
133	Daniel	Reker	Machine Learning to Predict Effectiveness of Inorganic Nanoparticles in Preclinical Cancer Research	View
134	Anne	Rodallec	PK/PD modelling to advance the preclinical development of a novel polymer prodrug in oncology	View
135	Maddalena	Sguizzato	Supramolecular delivery systems for polyphenols: prediction of in vivo diffusion and permeability	View
137	Kanika	Thakur	Demonstration of Virtual Bioequivalence (VBE) between Misoprostol Vaginal Rings Using Mechanistic Vaginal Absorption & Metabolism model (MechVAM) within Simcyp Simulator	View
138	Nadia	Toffoletto	Metformin-releasing contact lenses: predicting their in vivo efficacy using a physiology-based mathematical model	View
139	Andrew	Watson	Autoencoders to characterise and quantify variability in porcine skin	View
140	Paulina Anna	Wojtylo	MACHINE LEARNING-DRIVEN DEVELOPMENT OF INDOLE-3-ALDEHYDE DERIVATIVE THERAPEUTIC SYSTEMS	View
716	Dana	Meron Azagury	Prediction of Cancer Nanomedicines Self-Assembled from Meta-Synergistic Drug Pairs	View
1231	Yağmur	Pirincci Tok	Drying of Canagliflozin Nanosuspension by Top Spray Granulation and Spray Drying Methods and Optimizing the Procedure	View