The primary role of pharmacokinetic study in drug discovery is to aid in the creation and validation of tools that will help predict the pharmacokinetic behavior of a drug in humans. As drug development progresses, PK study evolves from predictive models to in vivo animal testing.
The goal throughout the process is to monitor the safety and efficacy of the drug. This information is used to create predictive models of the drug’s possible behavior in humans. Apart from the in vivo testing already mentioned, PK study also benefits from predictive computer models and in vitro metabolizing systems.
Pharmacokinetic Studies and the Body
Pharmacokinetics generally describes what the body does to the drug. This is usually achieved by monitoring ADME (Absorption, Distribution, Metabolism, and Excretion) mechanisms of a drug. PK sample are collected at regular intervals to check for various factors. These include bioavailability, maximum concentration Cmax, time to max concentration Tmax, and noncompartmental methods like the area under the curve of the concentration-time graph. Several other parameters become available as well.
Analysis of this data may be performed using noncompartmental and compartmental methods. In the application of noncompartmental methods, the area under curve (AUC) provides information on drug exposure. Compartmental methods use kinetic models to estimate the concentration-time graph.
This information gained through PK testing makes it possible to observe several characteristics of the drug. These can be used to decide the route of administration of the drug. For example, deciding whether the drug is best administered orally, dermally, intravenously, or through other means. Similarly, the data can be used to check the bioavailability and efficacy of the drug.
Other factors that become clear are safe dosage and dose formulation. Additionally, the data available with the PK study allows the creation of a more accurate model for translating the drug for human use.
Use of Pharmacokinetics in the Early Stages of Drug Discovery
PK data and modeling can be immensely useful in reducing the cost and time required for novel drug discovery. Simulation and models as part of PK testing speed up the identification of a prospective NCE (New Chemical Entity). These simulations are effective in predicting the properties of the chemical entity. This helps decide if the prospective drug or NCE will have therapeutic effects in humans.
Relying on existing data and conducting predictive analysis allows the development to proceed at a high speed. In several cases, the use of simulation can potentially shave off months off the potential development timeline. It is generally believed that in silico modeling still has many improvements to offer and its potential isn’t fully harvested. This implies a brighter future for computer modeling, and by extension, its use in the early stages of drug discovery.
Advances with in vitro and in silico tools have offered several advantages, allowing faster discovery. For example, analogs based on in vitro clearance data and predictive accuracy of high throughput in vitro tools can be used with in silico models. In these cases, in vitro data provides chemical series and in silico data can find applications in drug design.