Logic Engine for Smart Cancer Therapy Dosing
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Solution Overview
Problem
Current methods fail to effectively manage multiple drug interactions, particularly in cancer therapies, leading to potential harmful side effects and inadequate dosing, as they cannot analyze combinations of drugs being taken by patients.
Innovation Solution
A method and logic engine that collect and analyze patient data, including cancer therapies, using clinical trial data to determine safe and efficacious doses for each drug, considering drug-drug interactions and individual patient factors, while minimizing toxicity and maximizing therapeutic effect.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple drugs are administered to treat cancer, then therapeutic effect is improved, but risk of harmful side effects and drug interactions increases
Solution Approach 1:
The system performs preliminary analysis of drug interactions and dosing requirements before administering multiple cancer therapies. By pre-evaluating potential harmful interactions and calculating optimal dosages based on patient-specific factors, the system enables safe combination therapy while avoiding adverse effects.
Solution Approach 2:
The system dynamically adjusts drug dosage parameters based on patient response, drug interactions, and toxicity profiles. By continuously monitoring and modifying dosing parameters, the system maximizes therapeutic effect while minimizing harmful side effects from multiple drug administrations.
2Ease of operation
If standard dosing protocols are used for cancer therapies, then ease of administration is improved, but individual patient optimization is worsened
Solution Approach 1:
The system automatically calculates optimal dosages and generates personalized treatment regimens without requiring manual physician calculations. By self-generating patient-specific dosing protocols based on clinical trial data and patient characteristics, the system maintains ease of administration while achieving precise individualized dosing.
Solution Approach 2:
The system pre-calculates optimal dosages and treatment parameters before therapy initiation, storing these personalized protocols for straightforward administration. This preliminary optimization enables both precision dosing and ease of execution during actual treatment delivery.
3Device complexity
If drug interactions are not analyzed, then device complexity is reduced, but safety of treatment is worsened
Solution Approach 1:
The system introduces an intermediary software layer that automatically analyzes drug interactions between multiple cancer therapies. This intermediary component evaluates potential harmful interactions and provides recommendations without requiring complex manual analysis, thereby ensuring treatment safety while maintaining manageable system complexity.
Data Source
AI summary
A method of dosing cancer therapies, by collecting patient data including cancer therapies/drugs to be taken, analyzing the data in view of dosing criteria established based on outside data, and determining a dose for each cancer therapy/drug taken. A logic engine for dosing cancer therapies, including an algorithm stored on non-transitory computer readable media for collecting clinical trial data to establish criteria for dosing cancer therapies/drugs to a single patient and patient data and storing the clinical trial data and patient data in a database, analyzing the patient data in view of criteria established from the clinical trial data, and determining a dose for each cancer therapy/drug. A method of adjusting treatment of a cancer patient.