18579293. PREDICTION OF PHARMACOKINETIC CURVES simplified abstract (Hoffmann-La Roche Inc.)
Contents
PREDICTION OF PHARMACOKINETIC CURVES
Organization Name
Inventor(s)
[[:Category:Dominic Br�m of Basel (CH)|Dominic Br�m of Basel (CH)]][[Category:Dominic Br�m of Basel (CH)]]
Bernhard Steiert of Basel (CH)
PREDICTION OF PHARMACOKINETIC CURVES - A simplified explanation of the abstract
This abstract first appeared for US patent application 18579293 titled 'PREDICTION OF PHARMACOKINETIC CURVES
Simplified Explanation:
This patent application describes a computer-implemented method for predicting future points on a pharmacokinetic curve for a given species using machine learning models.
Key Features and Innovation:
- Receiving data representing concentration-time points of a pharmacokinetic curve
- Applying a machine learning model to generate subsequent concentration-time points
- Training machine learning models for prediction accuracy
Potential Applications: This technology can be used in pharmaceutical research and development to optimize drug dosing regimens and improve treatment outcomes.
Problems Solved: This technology addresses the challenge of accurately predicting future points on pharmacokinetic curves, which is crucial for drug development and personalized medicine.
Benefits:
- Enhanced accuracy in predicting pharmacokinetic profiles
- Improved drug dosing strategies
- Potential for personalized medicine advancements
Commercial Applications: Title: Predictive Pharmacokinetics Technology for Drug Development This technology can be utilized by pharmaceutical companies to streamline drug development processes, reduce costs, and enhance treatment efficacy in clinical settings.
Prior Art: Readers can explore existing literature on machine learning in pharmacokinetics, predictive modeling in drug development, and applications of AI in personalized medicine.
Frequently Updated Research: Stay informed about advancements in machine learning algorithms for pharmacokinetic predictions, new applications in drug development, and emerging trends in personalized medicine.
Questions about Predictive Pharmacokinetics Technology: 1. How does machine learning improve the accuracy of predicting future points on pharmacokinetic curves? 2. What are the potential implications of this technology for personalized medicine and patient care?
Original Abstract Submitted
A computer-implemented method of predicting at least one future point on a pharmacokinetic curve for a given species comprises: receiving an input comprising data representing a sequence of concentration-time points of a pharmacokinetic curve, each concentration-time point indicative of an amount of the given species in a subject's body at a respective time; applying a machine learning model to the input data, the machine learning model configured to generate an output comprising at least one subsequent concentration-time point in the pharmacokinetic curve. Computer-implemented methods of training machine learning models are also provided.
