INTERNATIONAL CONGRESS OF
PharmacoGenetics
Artificial Intelligence Platform for Personalized Drug Therapy Based on Patient Genetic Profiles: Integrating Pharmacogenomics and Genomics for Optimizing Treatment Response and Prevention
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Sahar Masoomi,1,*
1. Researcher in Artificial Intelligence, Medical Genetics, Pharmacogenomics, Pharmaceutical Biotechnology, and Psychology
- Introduction: With recent advancements in genomics and pharmacogenomics, precise personalized drug therapy based on individual genetic features has become increasingly feasible. This approach encompasses all genetic variants, disease types, drug sensitivities, and other patient-specific factors, which can be identified through molecular and genotypic analyses. Each individual can possess a comprehensive profile of genotype, drug-related polymorphisms, and potential drug sensitivities, generated through genetic testing and karyotyping. Artificial intelligence algorithms are capable of analyzing this complex data, identifying patterns of drug response, and providing recommendations for optimizing drug type and dosage. The primary mechanism of these platforms involves integrating genetic data with AI-based predictive models and machine learning algorithms, enabling patient stratification according to drug sensitivity risk, metabolic rates, and expected therapeutic responses. Through precise genomic and pharmacogenetic design, these platforms cover a wide spectrum of personal patient profiles, enabling broad and comprehensive personalized treatment strategies. Patients have access only to general information about their profile, while sensitive data and clinical decision-making remain restricted to healthcare professionals. The aim of this review is to provide a scientific framework for understanding the role of artificial intelligence in personalizing treatments, improving drug response prediction, reducing adverse effects, and enhancing therapeutic efficacy. Detailed mechanisms, including genetic and molecular pathways identified and modulated by AI algorithms, will be discussed in the results and conclusion sections to clarify the precise impact of these systems on therapeutic outcomes.
- Methods: This study is a systematic and analytical review of articles published between 2018 and 2025, focusing on the application of artificial intelligence in pharmacogenomics and genomics for personalized drug therapy. Articles were selected based on their direct relevance to the development of intelligent platforms, patient genetic profiles, treatment response optimization, and reduction of adverse drug effects. Database searches were conducted in PubMed, Scopus, Web of Science, and SpringerLink using keywords such as “Artificial Intelligence,” “Pharmacogenomics,” “Genomics,” “Personalized Medicine,” “Drug Response Optimization,” and “Precision Therapy.” Extracted data included AI algorithm design and performance, predictive drug response models, genetic profile analysis, coverage of diseases and drug sensitivities, and clinical or preclinical application examples. In addition to the literature review, this study emphasizes innovative concepts derived from integrating genomics and pharmacogenomics to create individualized patient profiles and provide therapeutic recommendations via intelligent platforms. This approach enables assessment of efficacy, safety, and drug response prediction prior to clinical application, and lays the groundwork for the development of AI-driven personalized therapies.
- Results: Analysis of the systematic review of articles and clinical evidence demonstrated that AI-driven platforms integrated with genomics and pharmacogenomics have the capacity to generate fully personalized drug profiles for each patient. These profiles encompass complete genetic variants, disease status, drug sensitivities, and pharmacodynamic and pharmacokinetic pathways. AI algorithms can analyze multi-layered omics data—including genomics, transcriptomics, proteomics, and metabolomics—to identify complex gene–drug interactions and predict drug responses. Evidence indicates that this approach reduces adverse effects, enhances therapeutic responses, and increases precision and drug safety. The mechanism of action of these platforms includes algorithmic analysis of genetic and metabolic signals, synchronization with clinical databases, and simulation of pharmacodynamic pathways. Furthermore, continuous updates of patient profiles enable precise and proactive clinical decision-making, flexibility in dose and drug selection, and dynamic optimization of treatment strategies.
- Conclusion: AI platforms integrated with genomics and pharmacogenomics provide a novel framework for precision medicine, offering fully personalized drug therapies. By analyzing comprehensive genetic profiles, drug sensitivities, and disease states, these systems enable optimized therapeutic regimens, accurate prediction of drug responses, and reduction of adverse effects. Restricted access for physicians and specialists ensures patient data privacy and enhances the safety of therapeutic decisions. The core mechanism involves predicting drug responses using machine learning and deep learning algorithms, simulating gene–drug interactions, and aligning with patient-specific pharmacokinetic and pharmacodynamic pathways. This approach not only optimizes existing treatments but also allows the design of preventive and long-term strategies for each patient, offering a new perspective on personalized drug therapy and patient lifetime management.
- Keywords: Advanced Artificial Intelligence, Personalized Pharmacogenomics, Clinical Genomics, Precision Medici
