Pharmacokinetic Modeling in Developing Countries: Utilizing Bloodstream Analysis to Evaluate Commonly Prescribed Drugs and Establishing Safe Use Practices

Utilizing models generated by machine learning, such as pharmacokinetic (PK) models in this case, this study will allow for the analysis of certain commonly used drugs in healthcare. It will also make way for the creation of safety precautions that can be implemented when these drugs are in use. To accomplish this, we will be modeling two specific drugs: Atorvastatin and Lisinopril. Our goal is to analyze how much of each drug remains in the bloodstream after use and the clinical benefits/harms that this may cause. Using this information, we can determine specific practices that must occur when these drugs are given to patients. By creating a pharmacokinetic model, we can see at what points/concentrations that the drugs are most effective. Based on these findings, we will develop evidence-based guidelines and dosing protocols tailored to the specific needs and conditions of patients in these regions, ensuring optimized therapeutic efficacy and minimized adverse effects. Collaborating with local health authorities, we will advocate for the integration of PK modeling findings into national drug policies and regulations and the establishment of monitoring systems to continuously evaluate and update safe use practices based on real-world data. By using bloodstream analysis, we can obtain precise data on drug concentrations and dynamics showing us proper dosage. The study will provide comprehensive guidelines and protocols for healthcare providers, improving drug safety and effectiveness. Integrating pharmacokinetic modeling into healthcare systems in developing countries can lead to better patient outcomes, informed policy decisions, and strengthened healthcare infrastructure.