Dose prediction methods are the approaches used to estimate the appropriate amount of a drug a patient should receive to achieve a therapeutic effect while minimizing the risk of toxicity. Because individuals differ in age, body size, organ function, genetics, and the presence of other medications or conditions, a s…
Dose prediction methods are the approaches used to estimate the appropriate amount of a drug a patient should receive to achieve a therapeutic effect while minimizing the risk of toxicity. Because individuals differ in age, body size, organ function, genetics, and the presence of other medications or conditions, a single fixed dose is not optimal for everyone. Dose prediction draws on pharmacokinetics, which describes how a drug is absorbed, distributed, metabolized, and eliminated, and on pharmacodynamics, which relates concentration to effect. Methods range from allometric and body-weight-based scaling and population pharmacokinetic modeling to physiologically based models and, increasingly, computational and machine-learning tools that integrate patient and drug data to guide individualized dosing. Within the journal's coverage of Advanced Pharmaceutical Science And Technology, dose prediction connects to computational modeling, toxicity assessment, and the broader goal of safe and effective drug use. This page gathers peer-reviewed, open-access research relevant to dose prediction methods, supporting readers interested in pharmacokinetic and pharmacodynamic modeling, individualized dosing, and the computational approaches used to anticipate how drugs behave across different patients and clinical situations.
1 peer-reviewed article, ranked by relevance. Each links to its DOI.