The PK/PD model integrates variables (like time, effect, etc.) and variation factors (such as gender, age, etc.) through modeling, sets the dose and degree of variation, fits the obtained data, and reveals the effects of drug by obtaining various parameters, as well as the changing characteristics of the process. The PK/PD modeling is helpful for drug screening and accelerates the process of new drug development. Therefore, PK/PD research has received more and more attention and has been widely used in all stages of drug development, preclinical and clinical research.
With the development and application of pharmacokinetic analysis software and computer technology, the methodology of PK/PD model establishment has also made great progress. Here is the use of 5 steps for modeling methodology research.
The PK/PD model obtains the following information by measuring the data of "blood drug concentration-time-effect":
The calculation methods of linear pharmacokinetic parameters mainly include: Simplex method, Gauss-Newton iteration method, and improved Gauss-Newton iteration methods such as Marquardt method and Hartley method. Among them, Marquardt method and Hartley method are the most commonly used. Many pharmaceutical programs include pharmacodynamic models, such as linear models, logarithmic models, and Sigmoid models (Hill’s equation). The logarithmic model is often used for microbial determination, and the Sigmoid Emax model can better describe most S-type dose-response relationships. There are several commonly used PK/PD model analysis programs at present.
The preclinical applications of PK/PD model include:
The PK/PD model can be used to predict the pharmacokinetic process of a new drug before in vivo testing. It helps to better analyze the characteristics of the pharmacokinetic model of the new drug, and establish a reasonable PK/PD model to assist the clinic in making drug screening decisions. Speculate the route of administration and dosage. MedAI has formed a team of experts excellent in PK/PD modeling, providing AI-driven solutions according to your detailed requirements.
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