Computer assisted prescribing of drugs

Area of Science:

• Pharmacology
• Medical Informatics
• Computational Biology

Background:

• Drug dosage adjustment is crucial for therapeutic efficacy and patient safety.
• Existing computer programs for drug dosing include fixed, adaptive, and empirical approaches.
• Predicting individual patient drug requirements presents challenges due to variable pharmacokinetic factors.

Purpose of the Study:

• To review different types of computer programs used for drug dosage adjustment.
• To discuss the predictability of drug dosage requirements for specific medications like aminoglycoside antibiotics and digoxin.
• To explore the potential and limitations of adaptive and empirical dosing strategies.

Main Methods:

• Review of existing literature on computer-assisted drug dosage adjustment programs.
• Analysis of the predictability of drug dosage based on patient-specific factors (e.g., body weight, creatinine clearance, compliance, absorption, liver clearance).
• Categorization of dosing programs into fixed, adaptive, and empirical methods based on their operational principles.

Main Results:

• Aminoglycoside antibiotic dosing is relatively predictable using fixed parameters like body weight and creatinine clearance.
• Digoxin dosing is less predictable due to variability in patient compliance, absorption, and liver clearance.
• Adaptive programs utilize drug concentration feedback for improved dosage prediction, while empirical methods leverage population data.

Conclusions:

• Adaptive computer programs offer improved personalized drug dosing by incorporating patient-specific feedback.
• Wider adoption of computer-assisted drug dosage programs is contingent upon expanding their capabilities and demonstrating clear clinical benefits.
• Further research is needed to enhance the scope and validate the efficacy of these computational tools in clinical practice.