INTERNATIONAL CONGRESS OF
PharmacoGenetics
AI-Assisted Personalized Drug Dosing Using Genomic
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kimiya kazemi esfeh,1,*
1. 3Department of Microbiology, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
- Introduction: Precision dosing has emerged as a cornerstone of personalized medicine, aiming to optimize therapeutic efficacy while minimizing toxicity. Inter-individual variability in drug response, driven by both genetic and non-genetic factors, poses significant challenges to conventional dosing regimens. Recent advances in artificial intelligence (AI) and pharmacogenomics provide unprecedented opportunities to refine drug dosing strategies tailored to individual patients.
- Methods: AI-assisted dosing models typically rely on supervised learning techniques, where labeled datasets (e.g., genotype, clinical features, drug doses, and therapeutic outcomes) are used to train predictive models. Common algorithms include: • Random Forests and Gradient Boosting Machines: Effective for tabular genomic and clinical data. • Neural Networks: Particularly deep neural networks (DNNs) can capture nonlinear relationships among high-dimensional features. • Bayesian Models: Integrate prior biological knowledge with data-driven insights. • Reinforcement Learning: Adaptive dosing strategies that evolve with patient feedback. Data sources include pharmacogenomic databases (e.g., PharmGKB), electronic health records (EHR AI-assisted dosing models typically rely on supervised learning techniques, where labeled datasets (e.g., genotype, clinical features, drug doses, and therapeutic outcomes) are used to train predictive models. Common algorithms include: • Random Forests and Gradient Boosting Machines: Effective for tabular genomic and clinical data. • Neural Networks: Particularly deep neural networks (DNNs) can capture nonlinear relationships among high-dimensional features. • Bayesian Models: Integrate prior biological knowledge with data-driven insights. • Reinforcement Learning: Adaptive dosing strategies that evolve with patient feedback. Data sources include pharmacogenomic databases (e.g., PharmGKB), electronic health records (EHR AI-assisted dosing models typically rely on supervised learning techniques, where labeled datasets (e.g., genotype, clinical features, drug doses, and therapeutic outcomes) are used to train predictive models. Common algorithms include: • Random Forests and Gradient Boosting Machines: Effective for tabular genomic and clinical data. • Neural Networks: Particularly deep neural networks (DNNs) can capture nonlinear relationships among high-dimensional features. • Bayesian Models: Integrate prior biological knowledge with data-driven insights. • Reinforcement Learning: Adaptive dosing strategies that evolve with patient feedback. Data sources include pharmacogenomic databases (e.g., PharmGKB), electronic health records (EHR AI-assisted dosing models typically rely on supervised learning techniques, where labeled datasets (e.g., genotype, clinical features, drug doses, and therapeutic outcomes) are used to train predictive models. Common algorithms include: • Random Forests and Gradient Boosting Machines: Effective for tabular genomic and clinical data. • Neural Networks: Particularly deep neural networks (DNNs) can capture nonlinear relationships among high-dimensional features. • Bayesian Models: Integrate prior biological knowledge with data-driven insights. • Reinforcement Learning: Adaptive dosing strategies that evolve with patient feedback. Data sources include pharmacogenomic databases (e.g., PharmGKB), electronic health records (EHR AI-assisted dosing models typically rely on supervised learning techniques, where labeled datasets (e.g., genotype, clinical features, drug doses, and therapeutic outcomes) are used to train predictive models. Common algorithms include: • Random Forests and Gradient Boosting Machines: Effective for tabular genomic and clinical data. • Neural Networks: Particularly deep neural networks (DNNs) can capture nonlinear relationships among high-dimensional features. • Bayesian Models: Integrate prior biological knowledge with data-driven insights. • Reinforcement Learning: Adaptive dosing strategies that evolve with patient feedback. Data sources include pharmacogenomic databases (e.g., PharmGKB), electronic health records (EHR AI-assisted dosing models typically rely on supervised learning techniques, where labeled datasets (e.g., genotype, clinical features, drug doses, and therapeutic outcomes) are used to train predictive models. Common algorithms include: • Random Forests and Gradient Boosting Machines: Effective for tabular genomic and clinical data. • Neural Networks: Particularly deep neural networks (DNNs) can capture nonlinear relationships among high-dimensional features. • Bayesian Models: Integrate prior biological knowledge with data-driven insights. • Reinforcement Learning: Adaptive dosing strategies that evolve with patient feedback. Data sources include pharmacogenomic databases (e.g., PharmGKB), electronic health records (EHR AI-assisted dosing models typically rely on supervised learning techniques, where labeled datasets (e.g., genotype, clinical features, drug doses, and therapeutic outcomes) are used to train predictive models. Common algorithms include: • Random Forests and Gradient Boosting Machines: Effective for tabular genomic and clinical data. • Neural Networks: Particularly deep neural networks (DNNs) can capture nonlinear relationships among high-dimensional features. • Bayesian Models: Integrate prior biological knowledge with data-driven insights. • Reinforcement Learning: Adaptive dosing strategies that evolve with patient feedback. Data sources include pharmacogenomic databases (e.g., PharmGKB), electronic health records (EHR
- Results: The era of precision medicine has redefined the paradigm of drug therapy, shifting from a "one-size-fits-all" approach to individualized treatment strategies. Among the critical components of this transformation is the concept of precision dosing—determining the right drug dose for the right patient at the right time. Despite well-established pharmacokinetic and pharmacodynamic models, traditional dosing often fails to account for the complex interplay of genetic, physiological, and environmental factors that influence drug metabolism and response.The era of precision medicine has redefined the paradigm of drug therapy, shifting from a "one-size-fits-all" approach to individualized treatment strategies. Among the critical components of this transformation is the concept of precision dosing—determining the right drug dose for the right patient at the right time. Despite well-established pharmacokinetic and pharmacodynamic models, traditional dosing often fails to account for the complex interplay of genetic, physiological, and environmental factors that influence drug metabolism and response.The era of precision medicine has redefined the paradigm of drug therapy, shifting from a "one-size-fits-all" approach to individualized treatment strategies. Among the critical components of this transformation is the concept of precision dosing—determining the right drug dose for the right patient at the right time. Despite well-established pharmacokinetic and pharmacodynamic models, traditional dosing often fails to account for the complex interplay of genetic, physiological, and environmental factors that influence drug metabolism and response.The era of precision medicine has redefined the paradigm of drug therapy, shifting from a "one-size-fits-all" approach to individualized treatment strategies. Among the critical components of this transformation is the concept of precision dosing—determining the right drug dose for the right patient at the right time. Despite well-established pharmacokinetic and pharmacodynamic models, traditional dosing often fails to account for the complex interplay of genetic, physiological, and environmental factors that influence drug metabolism and response.The era of precision medicine has redefined the paradigm of drug therapy, shifting from a "one-size-fits-all" approach to individualized treatment strategies. Among the critical components of this transformation is the concept of precision dosing—determining the right drug dose for the right patient at the right time. Despite well-established pharmacokinetic and pharmacodynamic models, traditional dosing often fails to account for the complex interplay of genetic, physiological, and environmental factors that influence drug metabolism and response.The era of precision medicine has redefined the paradigm of drug therapy, shifting from a "one-size-fits-all" approach to individualized treatment strategies. Among the critical components of this transformation is the concept of precision dosing—determining the right drug dose for the right patient at the right time. Despite well-established pharmacokinetic and pharmacodynamic models, traditional dosing often fails to account for the complex interplay of genetic, physiological, and environmental factors that influence drug metabolism and response.The era of precision medicine has redefined the paradigm of drug therapy, shifting from a "one-size-fits-all" approach to individualized treatment strategies. Among the critical components of this transformation is the concept of precision dosing—determining the right drug dose for the right patient at the right time. Despite well-established pharmacokinetic and pharmacodynamic models, traditional dosing often fails to account for the complex interplay of genetic, physiological, and environmental factors that influence drug metabolism and response.The era of precision medicine has redefined the paradigm of drug therapy, shifting from a "one-size-fits-all" approach to individualized treatment strategies. Among the critical components of this transformation is the concept of precision dosing—determining the right drug dose for the right patient at the right time. Despite well-established pharmacokinetic and pharmacodynamic models, traditional dosing often fails to account for the complex interplay of genetic, physiological, and environmental factors that influence drug metabolism and response.The era of precision medicine has redefined the paradigm of drug therapy, shifting from a "one-size-fits-all" approach to individualized treatment strategies. Among the critical components of this transformation is the concept of precision dosing—determining the right drug dose for the right patient at the right time. Despite well-established pharmacokinetic and pharmacodynamic models, traditional dosing often fails to account for the complex interplay of genetic, physiological, and environmental factors that influence drug metabolism and response.The era of precision medicine has redefined the paradigm of drug therapy, shifting from a "one-size-fits-all" approach to individualized treatment strategies. Among the critical components of this transformation is the concept of precision dosing—determining the right drug dose for the right patient at the right time. Despite well-established pharmacokinetic and pharmacodynamic models, traditional dosing often fails to account for the complex interplay of genetic, physiological, and environmental factors that influence drug metabolism and response.The era of precision medicine has redefined the paradigm of drug therapy, shifting from a "one-size-fits-all" approach to individualized treatment strategies. Among the critical components of this transformation is the concept of precision dosing—determining the right drug dose for the right patient at the right time. Despite well-established pharmacokinetic and pharmacodynamic models, traditional dosing often fails to account for the complex interplay of genetic, physiological, and environmental factors that influence drug metabolism and response.
- Conclusion: Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.Artificial intelligence (AI), particularly machine learning and deep learning models, has shown promise in handling high-dimensional data and uncovering complex patterns that traditional models may overlook. Integrating AI with genomic and clinical data enables the development of adaptive, data-driven dosing algorithms capable of learning from diverse patient populations.
- Keywords: artificial intelligence pharmacology
