Deep Learning for Non-Compartmental Pharmacokinetic Analysis of Sparse Data
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Solution Overview
Problem
Current approaches for estimating pharmacokinetic parameters, particularly in cases involving sparsely sampled measurements, are often rudimentary and result in inaccurate determinations, and conventional machine learning models fail to accurately predict these parameters when input sequences have variable lengths or non-standardized units.
Innovation Solution
A deep learning model, comprising convolutional layers and recurrent neural networks, is trained to predict pharmacokinetic parameters by normalizing input data and minimizing errors using ground truth values, enabling accurate predictions for both sparsely and densely sampled data, regardless of input sequence length or unit consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional machine learning models are used to predict pharmacokinetic parameters, then the model structure is relatively simple, but the prediction accuracy deteriorates when input sequences have variable lengths or non-standardized units
Solution Approach 1:
The patent employs a recurrent neural network (RNN) architecture that dynamically processes input sequences of variable lengths. The RNN maintains hidden states that adapt to different sequence lengths, allowing the model to handle diverse input formats without requiring fixed-length preprocessing. This dynamic processing capability resolves the contradiction by enabling accurate predictions regardless of input sequence variability.
Solution Approach 2:
The patent implements normalization layers that transform input features with non-standardized units into a consistent scale. By applying parameter transformations to standardize different pharmacokinetic measurements (e.g., concentration, time, dose), the model achieves uniform processing of heterogeneous inputs, thereby improving prediction accuracy without significantly increasing structural complexity.
2Productivity
If sparsely sampled pharmacokinetic data is used for analysis, then the data collection process is simpler and faster, but the determination accuracy of pharmacokinetic parameters deteriorates
Solution Approach 1:
The patent utilizes the RNN's inherent feedback mechanism where previous hidden states are fed into subsequent processing steps. This allows the model to accumulate information from sparse time points and infer missing pharmacokinetic behavior through temporal pattern recognition. The feedback loop enables accurate parameter determination even when input data is sparsely sampled, as the model compensates for missing information using learned temporal dynamics.
Solution Approach 2:
The patent applies preprocessing normalization and feature engineering before feeding sparse data into the model. By standardizing units and creating derived features in advance, the model receives optimized inputs that maximize the information content available from sparse measurements, thereby improving determination accuracy without requiring additional data collection.
3Measurement precision
If deep learning models with normalization and error minimization are implemented, then prediction accuracy improves, but computational resources and model complexity increase
Solution Approach 1:
The patent divides the deep learning model into distinct functional modules: normalization layers, recurrent processing units, and output prediction layers. Each module performs a specific function, making the overall complex system more manageable and interpretable. This segmentation allows the model to achieve high accuracy through specialized processing at each stage while maintaining a structured architecture that doesn't unnecessarily increase overall complexity.
4Reliability
If deep learning models are trained with ground truth values and error minimization, then prediction reliability improves, but training time and computational energy consumption increase
Solution Approach 1:
The patent employs continuous recurrent processing where the model maintains and updates hidden states across sequential time points. This continuous action allows the model to learn temporal patterns efficiently during training, achieving high reliability by consistently applying the same processing logic across all training samples. The recurrent architecture enables parallel processing of sequences, reducing overall training time compared to iterative methods that process each sample independently.
Data Source
AI summary
A method may include training a machine learning model to determine a first pharmacokinetic parameter and a second pharmacokinetic parameter for a molecule. The machine learning model may be trained by at least determining, based at least on an input including one or more pharmacokinetic models associated with the molecule, a first value of the first pharmacokinetic parameter, determining, based at least on the input, a second value of the first pharmacokinetic parameter, and determining, based at least on the first value and the second value of the first pharmacokinetic parameter, a third value of the second pharmacokinetic parameter. The method may also include applying the trained machine learning model to determine, based at least on a sparsely sampled pharmacokinetic model associated with the molecule, the first pharmacokinetic parameter and/or the second pharmacokinetic parameter. Related methods and articles of manufacture are also disclosed.