Deveeoping personaeized treatment guideeines with pharmacogenomics and precision medicine

Abstract

This invention presents a novel approach that integrates pharmacogenomics with multi-dimensional factor analysis to improve the detection of drug allergies and the adjustment of drug dosages tailored to individual patients. Genetic differences between individuals directly affect the production of proteins involved in the processes of absorption, distribution, metabolism, and excretion (ADME) of drugs in the body. This study has developed an analytical system that not only considers genetic factors but also includes external factors that influence drug efficacy, such as lifestyle habits, herbal consumption, and the effects of polypharmacy. The developed system utilizes a scoring process derived from the analysis of multiple data sources, including genetic data, treatment history, lifestyle information, and other clinical data. These scores assist in clinical decision making by evaluating the risk of adverse drug reactions and recommending appropriate drug dosage adjustments for each patient. This approach enhances the accuracy and safety of drug therapy.

Description

[0001] TITLE:

[0002] DEVEEOPING PERSONAEIZED TREATMENT GUIDEEINES WITH PHARMACOGENOMICS AND PRECISION MEDICINE

[0003] FIELD OF INNOVATION:

[0004] Field of engineering relates to Developing Personalized Treatment Guidelines with Pharmacogenomics and Precision Medicine: Assessing the Multifactorial Relationships Among Genes, Drugs, Herbs, and Lifestyle Factors to Enhance Therapeutic Efficacy and Safety BACKGROUND OF THE INVENTION:

[0005] Pharmacogenomics is a field that studies the relationship between an individual’s genetic makeup and their response to drugs. Currently, most drug therapies follow a "one-size-fits-all" approach, which does not account for the genetic variability among patients. This approach often leads to inconsistent drug responses and adverse drug reactions (ADRs). Therefore, the application of pharmacogenomic knowledge in medical treatments is critical to improving the efficacy and safety of drug usage. However, practical implementation of pharmacogenomics faces several challenges, including genetic data interpretation, integration of such data into clinical decision-making, and effective monitoring of treatment outcomes. Drug therapy remains a cornerstone of medical treatment. However, a significant challenge is the variability in individual drug responses, which can result in ADRs or suboptimal therapeutic outcomes. This issue adversely affects patients' quality of life and increases the burden on healthcare systems. The discovery of genetic factors influencing drug responses has given rise to the field of pharmacogenomics, which focuses on studying the effects of genetic variations — particularly in genes involved in drug metabolism, such as the CYP450 gene family, which plays a critical role in the metabolism of many drugs. Despite the high potential of pharmacogenomics in optimizing drug therapies, its adoption in clinical practice is limited due to the following reasons:

[0006] 1. Drug response is not determined solely by single-gene effects but requires the evaluation of the combined influence of multiple genes (polygenic effects) involved in drug response.

[0007] 2. Assessing drug response based solely on genetic factors is insufficient; other factors, such as age, sex, body weight, comorbidities, and concomitant medication, must also be considered. 3. Genetic testing is expensive and time-consuming, limiting its widespread use in clinical settings.

[0008] 4. Interpreting genetic data requires specialized expertise, which many healthcare professionals may lack.5. There is a lack of integrated clinical decision-making tools that combine data from multiple sources to assist healthcare providers.

[0009] Additionally, lifestyle factors significantly influence drug response but are often overlooked in dosage adjustment or drug selection. Integrating pharmacogenomic data with clinical and lifestyle factors is essential to developing personalized treatment guidelines tailored to individual patients. Such a system should be accurate, user-friendly, and capable of conveying results in a manner that is easily understood by medical professionals, enabling them to enhance drug efficacy and safety. The presented innovation aims to address these limitations by developing an analytical and decision-support system that leverages holistic data integration to advance drug therapy precision and safety.

[0010] CROSS-REFERENCES TO RELATED APPLICATIONS:

[0011] 1. US Patent No. US11710547B2: "Method and System for Providing Personalized Medicine to a Patient" This patent discloses a method and system for personalized medicine, involving the collection of data on two or more drugs, their dosages, and the patient's drug responses or therapeutic outcomes (e.g., blood pressure or blood sugar levels). The collected data is stored in a database for analysis and used to provide recommendations for subsequent medication usage. 2. US Patent No. US6087090A: "Methods for Predicting Drug Response" This patent describes methods for predicting drug responses and optimizing treatments by integrating pharmacogenomic data with information on drugs and other factors. It utilizes data from medication administration, drug dosages, and responses, including biochemical parameters measured pre- and post-treatment. This information is processed to generate drug recommendations.

[0012] 3. US Patent No. US20030023387A1 / 'Computer- Assisted Means for Assessing Lifestyle Risk Factors" This patent relates to a computer-assisted method for assessing disease susceptibility associated with lifestyle risk factors. The process involves collecting lifestyle risk data alongside genetic information linked to disease development. This data is analyzed to provide personalized recommendations for lifestyle and dietary modifications.

[0013] 4. US Patent No. US11710547B2 “Methods and Systems for Providing Personalized Medicine to a Patient” discloses a system and method for personalized medicine, specifically focusing on identifying and generating co-therapy regimens for patients. The system collects patientspecific data, including information about prescribed medications, patient responses, and physiological metrics such as blood pressure or blood glucose levels. This data is stored in a database and analyzed to optimize treatment regimens for each patient.SUMMARY OF THE INVENTION:

[0014] This invention is an advanced clinical decision support system that integrates Pharmacogenomics with multi-dimensional data analysis to deliver personalized treatment recommendations. It begins by collecting comprehensive patient data, including genetic information, clinical history, lifestyle factors, and medication usage. Using machine learning models, the system analyzes genetic variants that influence drug metabolism, transport, and targets, generating key scores such as Drug Efficacy Score and Adverse Event Risk Score. Additionally, it evaluates drug-drug, drug-herb, and drug-food interactions to prevent adverse effects. The system then provides personalized clinical recommendations, including optimal drug selection, dosage adjustments, and monitoring strategies, presented through an intuitive interface with visual risk indicators. Built with robust security measures like AES-256 encryption and multi-factor authentication, the system ensures data privacy. Continuous updates from real-world treatment data and the latest pharmacogenomic research enhance its predictive accuracy, making it a dynamic, intelligent tool for precision medicine.

[0015] DETAILED DESCRIPTION OF THE INVENTION

[0016] This invention introduces a fully integrated clinical decision support system that leverages Pharmacogenomics and multi-dimensional data analysis to optimize personalized treatment strategies. The system is structured as an interconnected workflow, where each component contributes to a seamless and intelligent decision-making process for patient care. The process begins with comprehensive data collection, where the system gathers patient-specific information from multiple sources. These include genetic data, obtained through DNA analysis with a focus on pharmacognosy — genes that influence drug metabolism, transport, and response. Additionally, the system incorporates clinical data, such as patient demographics (age, gender, weight, height, and BMI), underlying health conditions, and laboratory test results. It also retrieves medical history, including past and current medications, previous adverse drug reactions, and any known drug allergies. Further, the system accounts for lifestyle factors, including diet, exercise habits, smoking status, and alcohol consumption, as well as the use of herbs and dietary supplements, capturing details on the types, dosages, and frequency of their consumption. By integrating these diverse datasets, the system constructs a holistic patient profile, serving as the foundation for subsequent analysis. With the collected data, the system proceeds to genetic data analysis, focusing on pharmacogenomic insights. It examines genetic variants that affect drug metabolism, drug transport, and drug-target interactions. By referencing internationally recognized pharmacogenomics databases, such as PharmGKB and CPIC Guidelines, the system determines an individual’s ability to metabolize specific drugs, categorizing their metabolizer status as Poor,Intermediate, Extensive, or Ultra-rapid Metabolizer. This analysis also identifies variations in drug transport mechanisms that might impact absorption, distribution, and elimination, as well as genetic differences in drug target sites that could alter medication efficacy or safety. These findings are directly linked to clinical decision-making, providing the basis for drug selection and dosage adjustment tailored to the patient's genetic profile. To translate genetic and clinical data into actionable insights, the system employs machine learning models — such as Random Forest and Gradient Boosting — to develop a multi -factorial scoring system. These models analyze the complex relationships between genetic factors, medical history, lifestyle behaviors, and pharmacokinetic properties to compute two primary scores: the Drug Efficacy Score, which predicts the likelihood of a medication achieving its intended therapeutic effect in a specific patient, and the Adverse Event Risk Score, which estimates the probability of the patient experiencing an adverse drug reaction. These scores bridge genetic insights with real-world treatment applications, offering an evidence-based approach to medication optimization. Beyond genetic predispositions, the system also performs interaction analysis, identifying potential conflicts between different substances the patient is exposed to. This includes Drug-Drug Interactions (DDIs), where two or more prescribed medications may interfere with each other, leading to reduced efficacy or increased toxicity. The system also detects Drug-Herb Interactions, where traditional or alternative medicines might amplify or diminish the effects of pharmaceuticals. Additionally, Drug-Food Interactions are assessed, ensuring that dietary habits do not compromise medication absorption or therapeutic outcomes. By integrating a continuously updated interaction database, the system enhances prescription safety by proactively preventing harmful interactions before they occur. Using the outputs from genetic analysis, risk scoring, and interaction detection, the system generates personalized clinical recommendations. These recommendations provide healthcare professionals with clear, actionable guidance on optimal drug selection, ensuring that prescribed medications align with the patient’s genetic profile and clinical condition. Additionally, the system suggests precise dosage adjustments, tailoring drug concentrations to minimize adverse effects while maximizing efficacy. It also offers follow-up protocols, advising on appropriate monitoring strategies to track the patient's response to treatment and detect any emerging side effects. Moreover, the system recommends lifestyle modifications, such as dietary adjustments or behavioral changes, to further enhance treatment effectiveness. To ensure that these insights are easily interpretable, the system features an intuitive result display and communication interface. It presents information using localized language, ensuring accessibility for medical professionals in different regions. Additionally, graphical visualizations, such as heat maps and risk charts, provide a clear overview of drugefficacy and risk levels, enabling quick decision-making. Each recommendation is supplemented with scientific justifications, including references to peer-reviewed pharmacog enomics research, ensuring that clinicians have access to evidence-based reasoning for each decision. Given the sensitivity of patient data, the system incorporates robust security measures to safeguard privacy. AES-256 encryption is used for data storage, ensuring that all patient records remain protected from unauthorized access. For data transmission, the system utilizes TLS 1.3 encryption, preventing interception or tampering during communication. Furthermore, multi-factor authentication (MFA) ensures that only authorized personnel can access the system, enhancing security in healthcare settings. The system is designed for continuous improvement, with mechanisms for regular updates and refinements. It continuously collects real-world treatment data, using this information to enhance the accuracy of predictive models over time. Additionally, the system stays current with advancements in pharmacogenomics research, incorporating new findings into its databases. As new medications, herbal supplements, and dietary products enter the market, the system integrates these additions into its drug interaction and risk assessment frameworks, ensuring ongoing relevance and accuracy. By seamlessly linking data collection, genetic analysis, machine learning-based scoring, drug interaction detection, clinical recommendation generation, secure data management, and continuous updates, this invention establishes a dynamic and intelligent decision-support system. Each component is interdependent, forming a cohesive ecosystem that empowers clinicians with personalized, data-driven treatment strategies. This results in enhanced medication safety, improved therapeutic outcomes, and a more precise, patient-centric approach to healthcare.

[0017] DESCRIPTION OF DRAWINGS

[0018] Figure 1 Level of Pharmacogenomics Recommendation

[0019] Figure 2 Level of Pharmacogenomics Recommendations

[0020] Figure 3 Clinical Decision Support System: CDSS

[0021] Figure 4 The collaborative workflow of a multidisciplinary team in the process of pharmacogenomics and precision medicine, utilizing a pharmacogenomic clinical decision support system within the Electronic Medical Record (EHR) system.

[0022] Figure 5 The steps involved in pharmacogenomics (PGx), integrating genetic testing with medication management to enhance personalized treatment efficacy.

[0023] Figure 6 The system that analyzes pharmacogenomic data, evaluating multifactorial relationships between genes, medications, herbs, and lifestyle factors to provide personalized pharmacogenomic recommendations.

Claims

AMENDED CLAIMSreceived by the International Bureau on 05 June 2026 (05.06.2026)Claims

1. A SYSTEM OF DEVELOPING PERSONALIZED TREATMENT GUIDELINES WITH PHARMACOGENOMICS AND PRECISION MEDICINE: ASSESSING THE MULTIFACTORIAL RELATIONSHIPS AMONG GENES, DRUGS, HERBS, AND LIFESTYLE FACTORS TO ENHANCE THERAPEUTIC EFFICACY AND SAFETY, comprising: a module for prescribing medications and ordering pharmacogenomic tests by physicians;a module for analyzing pharmacogenetic (Pharmacogenes) data;a module for interpretation and recommendations provided by pharmacogenomic s specialists; a module for pre- and post-test patient consultations; a module for treatment analysis and planning by a multidisciplinary team; a Clinical Decision Support System (CDSS) for analyzing data and providing treatment adjustment recommendations; a module for pharmaceutical management based on pharmacogenomic s analysis results; and a module for continuous drug level monitoring and treatment adjustment.

2. A METHOD OF DEVELOPING PERSONALIZED TREATMENT GUIDELINES WITH PHARMACOGENOMICS AND PRECISION MEDICINE: ASSESSING THE MULTIFACTORIAL RELATIONSHIPS AMONG GENES, DRUGS, HERBS, AND LIFESTYLE FACTORS TO ENHANCE THERAPEUTIC EFFICACY AND SAFETY, comprising the steps of: prescribing medications and ordering pharmacogenomic tests by physicians; conducting genetic analysis in a laboratory; interpreting results and providing recommendations by pharmacogenomic s specialists; consulting patients about pharmacogenomic testing; analyzingand planning treatment by a multidisciplinary team; using aClinical Decision Support System to analyze data and provide recommendations; adjusting treatment based on pharmacogenomic analysis results; and monitoring drug levels in the bloodstream and continuously adjusting treatment.

3. The system as claimed in claim 1, wherein the Clinical Decision Support System utilizes machine learning algorithms for data analysis and recommendations.

4. The method as claimed in claim 2, further comprising the use of clinical data in addition to genetic data for treatment adjustments.

5. The system as claimed in claim 1, wherein the system is integrated with electronic medical records (EMR) for efficient data exchange.

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